Relationship of Acute Ischemic and Hemorrhagic Stroke with Weather and its Parameters.

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Waseem Dar, Maqbool Wani, Arjimand Yaqoob, Zubair Khuja, Amit Chandra, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7727415/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 6 You are reading this latest preprint version Abstract Background: Stroke is a leading cause of mortality and disability worldwide. While traditional risk factors are well-established, the role of meteorological parameters like temperature, humidity, atmospheric pressure, and wind speed remains unclear. This study investigates the relationship between acute ischemic stroke (AIS) and intracerebral hemorrhage (ICH) with weather in the Kashmir Valley, known for its extreme winters. A 25-month prospective study (January 2020–January 2022) included 1,144 stroke patients admitted to a tertiary care center. Strokes were classified as ischemic or hemorrhagic based on CT imaging. Meteorological data for stroke onset days were retrieved from the Indian Meteorological Department. Associations between weather variables and stroke subtypes were analyzed using multivariate regression models. Results : Of 1,144 patients, 52.9% (605) had ICH, and 47.2% (540) had AIS. Stroke incidence peaked in winter, especially in January. Higher atmospheric pressure and wind speed were associated with increased ICH risk but reduced AIS risk. Temperature and humidity had no significant effect on either subtype. Hypertension was the leading cause of ICH, with the putamen most affected. Cardio-embolic strokes were the predominant AIS subtype, showing seasonal variation. Conclusion : Meteorological factors, particularly atmospheric pressure and wind speed, influence stroke risks differently for AIS and ICH. Extreme weather conditions may increase stroke risk, especially for hemorrhagic strokes. Public health strategies, such as advising at-risk individuals to limit exposure to harsh winters, could reduce stroke incidence in regions with extreme climates. Stroke Ischemic stroke Hemorrhagic stroke Weather Meteorological factors Kashmir Valley Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke stands as the second leading cause of death worldwide and ranks third when considering both death and disability combined ( 1 ). Globally, about one in four adults aged 25 years or older is expected to suffer a stroke at some point in their lives ( 2 ). Ischemic stroke, the most prevalent subtype, accounted for 62.4% of all strokes in 2019, while intracerebral haemorrhage and subarachnoid haemorrhage made up 27.9% and 9.7%, respectively ( 1 ). Nevertheless, stroke epidemiology varies significantly across different regions. In Western countries, ischemic strokes constitute 80–85% of all cases, whereas in India, the proportion ranges from 65% in Kolkata to 84% in Trivandrum, where intracerebral haemorrhage accounts for 11% and 35%, respectively ( 3 ). Interestingly, haemorrhagic strokes have been reported as the most common subtype in Jammu & Kashmir ( 4 ). Traditional risk factors are responsible for 60–80% of stroke cases, leaving a significant portion influenced by non-traditional factors. The INTERSTROKE study highlighted 10 major contributors to stroke risk, including less conventional ones such as obesity, psychosocial stress, depression, and the apolipoprotein B/A1 ratio, which together account for 90% of the overall stroke risk ( 5 ). Additional non-traditional factors include metabolic syndrome, sleep apnea, chronic inflammation, chronic kidney disease, dietary patterns, and environmental exposures, such as weather conditions, which may serve as acute triggers for stroke onset. Numerous studies have demonstrated a link between meteorological factors, such as temperature, humidity, atmospheric pressure, and sunlight exposure, and the incidence of stroke ( 6 ). This relationship is particularly pertinent to the Kashmir Valley, which experiences harsh winters with temperatures dropping to -7°C and moderate summers reaching up to 37°C. In light of these distinct climatic conditions, we undertook this study to investigate the association between ischemic and haemorrhagic strokes and various meteorological factors. Methods This study received approval from the Institutional Ethics Committee of the Hospital. Over a 25-month period (January 2020–January 2022), all patients admitted to our department with a confirmed diagnosis of acute stroke were included after obtaining informed consent from either the patient or their immediate family members. Data were systematically collected using a structured proforma (provided as a Supplementary File). Patients diagnosed with subarachnoid haemorrhage, subdural haemorrhage, or extradural haemorrhage were excluded, along with stroke mimics—cases initially presumed to be acute stroke but later confirmed as other conditions. Enrolled patients were classified as having either ischemic or haemorrhagic stroke based on the findings of an emergency CT scan conducted upon admission. Ischemic stroke cases were further categorized using the TOAST (Trial of Org 10172 in Acute Stroke Treatment) classification system ( 7 ). The term "weather" refers to the temporary atmospheric conditions within the Earth's air layer. It consists of six key elements: temperature, atmospheric pressure, cloud cover, wind, humidity, and precipitation. Even minor variations in these factors can lead to distinct weather patterns. Temperature reflects how hot or cold the atmosphere is, while atmospheric pressure represents the weight of the air above. High-pressure systems are typically associated with cooler temperatures and clear skies, whereas low-pressure systems often bring warmer conditions, storms, and rain. Wind denotes the movement of air caused by differences in temperature and atmospheric pressure across regions, and humidity measures the concentration of water vapour in the air. Meteorological data for this study were provided by the Indian Meteorological Department ( 8 ). The analysis focused on meteorological factors recorded on the day of the stroke, including minimum temperature (°C), maximum temperature (°C), relative Humidity (%), wind speed (km/s) and atmospheric pressure (hPa). Data were coded and recorded using the MS Excel spreadsheet program, and statistical analyses were conducted using SPSS version 23 (IBM Corp.). Descriptive statistics were reported as means with standard deviations or medians with interquartile ranges (IQRs) for continuous variables, and as frequencies and percentages for categorical variables. Data visualization included graphical methods such as histograms, box-and-whisker plots, and column charts for continuous data, as well as bar charts and pie charts for categorical data, as appropriate. Group comparisons for continuous variables were performed using the independent sample t-test for normally distributed data, while the Wilcoxon test was applied as a non-parametric alternative for non-normally distributed data. The Chi-squared test was employed to compare categorical variables between groups, with Fisher’s Exact test used when expected frequencies in contingency tables were < 5 in over 25% of cells. Correlations between continuous variables were assessed using Pearson’s correlation for normally distributed data and Spearman’s correlation for non-normally distributed data. Multivariate Poisson generalized linear regression models were applied to estimate relative risks (RRs) with 95% confidence intervals (CIs) and identify independent meteorological factors associated with the occurrence of intracerebral haemorrhage (ICH) or acute ischemic stroke (AIS). A p-value of < 0.05 was considered statistically significant. Results The study was conducted over a period of 25 months from January 2020 to January 2022. A total of 1144 patients were included in the study. The baseline characteristics of the patients are given in Table 1 . The mean age of the study group was 61.81 (SD 13.22) years. 605 (52.9%) of the participants had haemorrhagic stroke whereas 540 (47.2%) of the participants had ischemic stroke. Overall strokes were most common in the month of January and in the winter season. Table 2 gives the distribution of average monthly meteorological variables in haemorrhagic and ischemic strokes of our study. Table 1 Baseline Characteristics of all stroke patients Clinical Details Number (Percentages) Gender Male 684 (59.8%) Female 460 (40.2%) Residence Urban 281 (24.5%) Rural 863 (75.5%) Prior History of Hypertension 883 (77.4%) Compliance to Antihypertensive Drugs 413 (46.0%) Regular BP Monitoring 65 (7.2%) Previous Diabetic History 198 (17.3%) Compliance to Anti-Diabetic Treatment 157 (86.7%) Regular Monitoring of Diabetes 47 (26.1%) Previous History of AF 77 (6.7%) Previous Anticoagulation Use 47 (4.0%) Type of Anticoagulation Vitamin K Antagonists 21 (44.7%) Novel Oral Anticoagulants 26 (55.3%) Compliance to Anticoagulation No 17 (36.2%) Yes 10 (21.3%) Inadequate Dose 20 (42.6%) Past Stroke: Ischemic Stroke 99 (8.7%) Past Stroke: Haemorrhagic Stroke 50 (4.4%) Table 2 Descriptive analysis of monthly stroke occurrence and average monthly meteorological factors. Month Total Strokes ICH AIS Mean Min Temp ( 0 C) Mean Max Temp ( 0 C) RH (%) SLP (hPa) Windspeed (kmph) January 162 76 86 -1.69 ± 2.39 5.29 ± 2.50 81.78 ± 16.06 843.71 ± 3.79 1.42 ± 0.86 February 100 55 45 1.44 ± 3.38 12.05 ± 4.70 74.05 ± 19.94 843.14 ± 3.85 2.20 ± 0.85 March 104 59 45 5.13 ± 2.77 14.65 ± 4.04 73.20 ± 24.36 841.64 ± 3.63 2.95 ± 1.66 April 68 33 35 7.81 ± 2.28 19.52 ± 4.51 65.68 ± 25.72 840.74 ± 3.90 3.22 ± 1.79 May 105 83 22 10.83 ± 1.58 24.70 ± 4.39 60.50 ± 10.92 839.19 ± 2.70 3.53 ± 1.23 June 114 75 39 14.47 ± 2.23 28.63 ± 3.81 57.10 ± 16.13 835.58 ± 3.63 3.51 ± 1.32 July 98 50 48 18.57 ± 2.46 30.41 ± 2.78 58.24 ± 10.77 833.37 ± 3.47 3.08 ± 1.32 August 82 30 52 18.23 ± 2.79 29.65 ± 3.26 57.09 ± 23.71 833.61 ± 2.47 2.54 ± 1.38 September 77 33 44 14.79 ± 2.65 28.95 ± 1.77 59.65 ± 13.68 836.87 ± 3.39 2.32 ± 1.13 October 80 44 36 10.15 ± 4.58 21.73 ± 3.95 62.91 ± 14.67 840.73 ± 2.28 1.64 ± 0.92 November 45 19 26 -1.11 ± 3.50 11.81 ± 4.34 81.09 ± 13.50 843.73 ± 3.19 0.98 ± 0.99 December 109 48 61 -1.88 ± 2.01 8.90 ± 1.76 69.65 ± 16.46 843.70 ± 2.17 1.00 ± 0.82 ICH: Intracranial Hemorrhage; AIS: Acute Ischemic Stroke; RH: Relative Humidity; SLP: Sea-level Pressure. Putamen was the most common site of haemorrhagic stroke followed by thalamus and lobar haemorrhages. 56.3% of haemorrhages were on the left side, 42.3% on the right side and 1.4% bilateral. The mean volume (SD) was 28.23 (26.84) ml. The median volume (IQR) was 20.00 (10–35) ml and the range was 1–200ml. 35.8% (n = 216) had intraventricular extension. The majority of the patients had a lower ICH score placing them in a good prognostic category group. Most of the patients had hypertensive haemorrhages. The Box-and-Whisker plot (Fig. 1 ) depicts the distribution of the volume of ICH (ml) in each month. There was no significant association between the volume of haemorrhage and the month of the year (χ2 = 17.055, p = 0.106). The density plot (Fig. 2 ) depicts the distribution of ICH Score in the 12 different months of the year. There was no significant difference between the months in terms of ICH Score (χ2 = 18.250, p = 0.076). Table 3 & Fig. 3 summarize the regression analysis for haemorrhagic strokes using all the predictor variables together in one go. The 'OR (univariable)' column lists the odds ratios for each of the variables with respect to the haemorrhagic stroke when these variables are used as single predictors of the dependent variable, without entering the rest of the variables in the model. The 'OR (multivariable)' column lists the odds ratios for all the variables when they are entered in the model together (and are now thus controlling for each other). Table 3 Univariate and Multivariate analysis of various weather variables and haemorrhagic stroke. Haemorrhagic Stroke OR (univariable) OR (multivariable) Minimum Temp (C) Mean (SD) 7.9 (7.6) 1.01 (0.99–1.02, p = 0.301) 1.00 (0.96–1.04, p = 0.863) Maximum Temp (C) Mean (SD) 19.4 (9.6) 1.01 (1.00-1.02, p = 0.115) 1.01 (0.98–1.04, p = 0.476) Relative Humidity (%) Mean (SD) 66.7 (19.0) 1.00 (0.99-1.00, p = 0.396) 1.00 (0.99-1.00, p = 0.385) Windspeed (Km/s) Mean (SD) 2.6 (1.5) 1.21 (1.12–1.32, p < 0.001) 1.23 (1.12–1.35, p < 0.001) SLP (HPA) Mean (SD) 839.9 (4.6) 1.01 (0.99–1.03, p = 0.432) 1.04 (1.00-1.08, p = 0.036) Amongst the ischemic stroke subtypes (TOAST Classification), Cardio embolic strokes were the most common followed by strokes of undetermined aetiology. Figure in Supplementary file 1 shows distribution of various ischemic stroke subtypes in different months of the year. Cardio embolic strokes were most common in the month of December while artery to artery strokes were most common in August and small artery strokes in April. Table 4 & Fig. 4 summarize the regression analysis for ischemic strokes using all the predictor variables together in one go. The 'OR (univariable)' column lists the odds ratios for each of the variables with respect to the ischemic stroke, when these variables are used as single predictors of the dependent variable, without entering the rest of the variables in the model. The 'OR (multivariable)' column lists the odds ratios for all the variables when they are entered into the model together (and are now thus controlling for each other). Table 4 Univariate and Multivariate analysis of various weather variables and ischemic stroke. Ischemic Stroke OR (univariable) OR (multivariable) Minimum Temp (C) Mean (SD) 7.4 (8.3) 0.99 (0.98–1.01, p = 0.264) 1.00 (0.96–1.04, p = 0.860) Maximum Temp (C) Mean (SD) 18.4 (9.9) 0.99 (0.98-1.00, p = 0.098) 0.99 (0.96–1.02, p = 0.452) Relative Humidity (%) Mean (SD) 67.7 (20.5) 1.00 (1.00-1.01, p = 0.415) 1.00 (1.00-1.01, p = 0.432) Windspeed (Km/s) Mean (SD) 2.2 (1.4) 0.82 (0.76–0.89, p < 0.001) 0.81 (0.74–0.89, p < 0.001) SLP (HPA) Mean (SD) 839.7 (5.4) 0.99 (0.97–1.01, p = 0.472) 0.96 (0.92-1.00, p = 0.038) Discussion Non-traditional risk factors have gained increasing attention in the context of stroke risk, particularly as multiple studies indicate that a significant proportion of strokes cannot be attributed solely to traditional risk factors such as hypertension, diabetes mellitus, and heart disease. Furthermore, non-traditional risk factors are known to exacerbate traditional ones, underscoring the importance of their evaluation and management. For instance, research has demonstrated that cold weather can elevate blood pressure, primarily through vasoconstriction, thereby increasing stroke risk ( 9 ). However, the relationship between cold weather and stroke extends beyond blood pressure, involving other mechanisms such as effects on coagulation pathways and endothelial function ( 10 ). These combined effects may significantly amplify stroke risk during colder seasons. Advising high-risk individuals to relocate to warmer areas during the winter months could be a practical recommendation, particularly in regions like the Kashmir Valley, where winters are harsh, prolonged, and frequently marked by sub-zero temperatures. Implementing such measures could help mitigate the elevated stroke risk associated with cold climates. Many risk factors, such as hypertension, are shared between haemorrhagic and ischemic strokes. However, certain risk factors are specific to each subtype, which suggests that the influence of weather and its elements may not be uniform across all stroke types. In our study, we examined the relationship between stroke and four weather elements: temperature, atmospheric pressure, humidity, and wind speed. The effect of temperature on stroke risk remains a topic of debate. While most studies suggest that lower temperatures are associated with an increased stroke risk, some have reported no such relationship, and others have observed differing impacts on haemorrhagic and ischemic strokes ( 10 – 13 ). In our study, neither high nor low temperatures were found to significantly increase the risk of either ischemic or haemorrhagic stroke. The reasons why lower temperatures may elevate stroke risk, as shown in many studies, are not entirely understood. Some researchers suggest that cold-induced vasoconstriction raises blood pressure, thereby increasing stroke risk, while others propose that heightened blood coagulability during winter could play a role. Additionally, colder seasons often lead to reduced physical activity, as people tend to stay indoors. However, the increased stroke risk observed during winter may not be solely attributable to lower temperatures. For instance, in the U.S., stroke mortality rates are highest in the Southeast, a region warmer than much of the country but characterized by lower physical activity levels, higher obesity rates, and increased smoking prevalence ( 14 ). This highlights that temperature alone does not fully explain the elevated stroke risk observed during colder seasons. Other environmental and behavioural factors likely contribute to the seasonal variation in stroke incidence. Relative humidity (RH) measures the amount of water vapor in the air relative to the maximum amount the air can hold at a given temperature. Warmer air holds more water vapor, while cooler air retains less, which explains why summers are typically hot and humid, whereas winters tend to be cold and dry. Consequently, the summer season generally experiences higher humidity levels than other seasons. The impact of relative humidity on stroke risk remains a subject of debate. Some studies have reported a significant association between relative humidity and stroke risk, while others have produced contradictory findings ( 15 – 18 ). In our study, no significant association was observed between relative humidity and the risk of either hemorrhagic or ischemic stroke. High humidity, however, can lead to dehydration and heat stress, potentially impairing blood pressure regulation and vascular function ( 19 ). These physiological stresses might contribute to an increased stroke risk under certain conditions. The relationship between relative humidity and stroke is influenced by multiple factors, including physiological responses, seasonal variations, and broader environmental conditions. While some evidence supports a connection between humidity and stroke risk, further research is required to elucidate the underlying mechanisms and establish clearer links. Wind speed, another weather parameter examined in our study, arises from the movement of air between areas of high and low pressure, primarily influenced by temperature changes. The relationship between wind speed and stroke has garnered increasing attention in epidemiological research. While the direct effects of wind speed on stroke are not well-documented, several factors suggest a potential link ( 20 ). Higher wind speeds can affect the dispersion of air pollutants, and exposure to poor air quality has been associated with an increased risk of stroke. Sudden changes in weather, including shifts in wind speed, may lead to vascular instability. Research also indicates that strong winds could trigger acute cardiovascular events, including strokes. In our study, we observed a significant association between wind speed and stroke risk. Specifically, higher wind speeds were linked to an increased risk of hemorrhagic stroke and a decreased risk of ischemic stroke. The underlying reasons for this contrasting effect remain unclear. Elevated wind speeds have been shown to increase blood pressure, which may act as a precipitating factor for hypertension-related hemorrhagic strokes ( 21 ). Wind conditions also influence outdoor physical activity levels. Reduced activity during adverse weather may contribute to stroke risk factors such as obesity and hypertension ( 22 ). Additionally, windy conditions may heighten psychological stress, a recognized risk factor for stroke. Stress can elevate blood pressure and heart rate, further increasing stroke risk. Atmospheric pressure, or air pressure, is the force exerted by the weight of air on a surface within the atmosphere. It significantly impacts weather patterns, with high-pressure systems often bringing clear skies and calm conditions, while low-pressure systems are linked to clouds, precipitation, and storms. Additionally, atmospheric pressure affects environmental processes such as wind patterns, ocean currents, and pollutant dispersion ( 23 ). The relationship between atmospheric pressure and stroke has been investigated in numerous studies. Research suggests that individuals living at high altitudes, where atmospheric pressure is lower, may have a reduced risk of stroke. This could be attributed to lower hypertension rates and increased physical activity at higher elevations ( 24 ). However, these findings are not universally consistent. Several studies indicate that reduced atmospheric pressure may increase stroke risk, particularly in individuals with pre-existing health conditions, possibly due to lower oxygen levels (hypoxia) ( 25 ). Furthermore, fluctuations in barometric pressure have been associated with a rise in stroke events, likely through their effects on cerebral blood flow and vascular function ( 26 ). In our study, we observed that higher atmospheric pressure was linked to an increased risk of haemorrhagic stroke and a decreased risk of ischemic stroke. This suggests that the relationship between atmospheric pressure and stroke risk is multifaceted and may be influenced by various environmental factors. Summary The relationship between weather and stroke risk has been a subject of extensive research, revealing several ways in which environmental factors can influence stroke occurrence. The relationship between stroke risk and individual weather parameters is complex and not entirely clear. Weather encompasses a variety of parameters that describe the state of the atmosphere at a given time and place. Thus as a whole weather may play a significant impact on stroke risk. Cold weather is particularly associated with elevated stroke risk and this is particularly relevant in Kashmir Valley where winters can be harsh. Advising people at high propensity of developing a stroke to move to warmer areas of the country during winter seasons can be a form of health education as well as a component of state health care policy. Declarations Author Contribution M.W: Conceptualization, Methodology: W.D: Data curation, Writing- Original draft preparation. A.Y, Z.K, F.M: Visualization, Investigation. R.A, Z.P: Supervision.: I.B, A.C: Software, Validation. A.R: Reviewing and Editing References Feigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG, et al. 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Zheng B, Luo Y, Li Y, Gu G, Jiang J, Chen C, et al. Prevalence and risk factors of stroke in high-altitude areas: a systematic review and meta-analysis. BMJ Open. 2023 Sep 1;13(9):e071433. Honig A, Eliahou R, Pikkel YY, Leker RR. Drops in Barometric Pressure Are Associated with Deep Intracerebral Hemorrhage. J Stroke Cerebrovasc Dis. 2016 Apr 1;25(4):872–6. Additional Declarations No competing interests reported. Supplementary Files Supp1.docx Proforma.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 Feb, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 02 Jan, 2026 Submission checks completed at journal 01 Jan, 2026 First submitted to journal 30 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7727415","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568555416,"identity":"31b2a3e8-3a9a-48d8-a722-7acb60ecdb0a","order_by":0,"name":"Waseem 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06:17:47","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94453,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/eb3bf6477da932c6b3a1fe23.html"},{"id":99495584,"identity":"2afa20e5-b661-4fba-aaea-8babcdc5ed76","added_by":"auto","created_at":"2026-01-05 06:17:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89449,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between month and volume of ICH\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/eceee45a21c78e23bb512011.png"},{"id":99790518,"identity":"6d654b87-1fbe-4689-bfbb-67ce4d130292","added_by":"auto","created_at":"2026-01-08 12:58:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":252805,"visible":true,"origin":"","legend":"\u003cp\u003eDensity plot showing the association between ICH score and month of the year.\u003c/p\u003e\n\u003cp\u003eLegends\u003c/p\u003e\n\u003cp\u003e1. Figure 2—\u003c/p\u003e\n\u003cp\u003eThe middle horizontal line represents the median ICH volume, the upper and lower bounds of the box represent the 75th and the 25th centile of ICH volume respectively, and the upper and lower extent of the whiskers represent the Tukey limits for ICH volume in each month.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/9c7e47d1084cfa6ee727fe91.png"},{"id":99495586,"identity":"a840d041-8fcb-463c-a3d0-685814ac0e21","added_by":"auto","created_at":"2026-01-05 06:17:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85623,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate analysis of various weather variables and haemorrhagic stroke.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/97a0130684ab419c0eb119f7.png"},{"id":99791485,"identity":"c305cf14-ab6c-4e16-8a87-587d5a188cc8","added_by":"auto","created_at":"2026-01-08 13:00:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90194,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate analysis of various weather variables and ischemic stroke\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/7fa53af60a233437d2e3667c.png"},{"id":99803048,"identity":"502dff01-3664-4b1d-9c4b-e61290be06ba","added_by":"auto","created_at":"2026-01-08 14:09:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1218803,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/ac67bdb4-e2c9-4a2c-be59-d689e90f89b6.pdf"},{"id":99495593,"identity":"e60f82bd-dcb0-4fe7-9c96-80970990601d","added_by":"auto","created_at":"2026-01-05 06:17:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":184285,"visible":true,"origin":"","legend":"","description":"","filename":"Supp1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/f5b2ddbf000cedcef0d5a9e2.docx"},{"id":99790923,"identity":"ea1275fc-f380-48f5-b3c4-5ce84a20d6d2","added_by":"auto","created_at":"2026-01-08 12:58:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13899,"visible":true,"origin":"","legend":"","description":"","filename":"Proforma.docx","url":"https://assets-eu.researchsquare.com/files/rs-7727415/v1/b8d7142991bc55eb0c0eff39.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship of Acute Ischemic and Hemorrhagic Stroke with Weather and its Parameters.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke stands as the second leading cause of death worldwide and ranks third when considering both death and disability combined (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Globally, about one in four adults aged 25 years or older is expected to suffer a stroke at some point in their lives (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Ischemic stroke, the most prevalent subtype, accounted for 62.4% of all strokes in 2019, while intracerebral haemorrhage and subarachnoid haemorrhage made up 27.9% and 9.7%, respectively (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Nevertheless, stroke epidemiology varies significantly across different regions. In Western countries, ischemic strokes constitute 80\u0026ndash;85% of all cases, whereas in India, the proportion ranges from 65% in Kolkata to 84% in Trivandrum, where intracerebral haemorrhage accounts for 11% and 35%, respectively (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Interestingly, haemorrhagic strokes have been reported as the most common subtype in Jammu \u0026amp; Kashmir (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditional risk factors are responsible for 60\u0026ndash;80% of stroke cases, leaving a significant portion influenced by non-traditional factors. The INTERSTROKE study highlighted 10 major contributors to stroke risk, including less conventional ones such as obesity, psychosocial stress, depression, and the apolipoprotein B/A1 ratio, which together account for 90% of the overall stroke risk (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Additional non-traditional factors include metabolic syndrome, sleep apnea, chronic inflammation, chronic kidney disease, dietary patterns, and environmental exposures, such as weather conditions, which may serve as acute triggers for stroke onset.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated a link between meteorological factors, such as temperature, humidity, atmospheric pressure, and sunlight exposure, and the incidence of stroke (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This relationship is particularly pertinent to the Kashmir Valley, which experiences harsh winters with temperatures dropping to -7\u0026deg;C and moderate summers reaching up to 37\u0026deg;C. In light of these distinct climatic conditions, we undertook this study to investigate the association between ischemic and haemorrhagic strokes and various meteorological factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This study received approval from the Institutional Ethics Committee of the Hospital. Over a 25-month period (January 2020\u0026ndash;January 2022), all patients admitted to our department with a confirmed diagnosis of acute stroke were included after obtaining informed consent from either the patient or their immediate family members. Data were systematically collected using a structured proforma (provided as a Supplementary File). Patients diagnosed with subarachnoid haemorrhage, subdural haemorrhage, or extradural haemorrhage were excluded, along with stroke mimics\u0026mdash;cases initially presumed to be acute stroke but later confirmed as other conditions.\u003c/p\u003e \u003cp\u003eEnrolled patients were classified as having either ischemic or haemorrhagic stroke based on the findings of an emergency CT scan conducted upon admission. Ischemic stroke cases were further categorized using the TOAST (Trial of Org 10172 in Acute Stroke Treatment) classification system (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe term \"weather\" refers to the temporary atmospheric conditions within the Earth's air layer. It consists of six key elements: temperature, atmospheric pressure, cloud cover, wind, humidity, and precipitation. Even minor variations in these factors can lead to distinct weather patterns.\u003c/p\u003e \u003cp\u003eTemperature reflects how hot or cold the atmosphere is, while atmospheric pressure represents the weight of the air above. High-pressure systems are typically associated with cooler temperatures and clear skies, whereas low-pressure systems often bring warmer conditions, storms, and rain. Wind denotes the movement of air caused by differences in temperature and atmospheric pressure across regions, and humidity measures the concentration of water vapour in the air.\u003c/p\u003e \u003cp\u003eMeteorological data for this study were provided by the Indian Meteorological Department (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The analysis focused on meteorological factors recorded on the day of the stroke, including minimum temperature (\u0026deg;C), maximum temperature (\u0026deg;C), relative Humidity (%), wind speed (km/s) and atmospheric pressure (hPa).\u003c/p\u003e \u003cp\u003eData were coded and recorded using the MS Excel spreadsheet program, and statistical analyses were conducted using SPSS version 23 (IBM Corp.). Descriptive statistics were reported as means with standard deviations or medians with interquartile ranges (IQRs) for continuous variables, and as frequencies and percentages for categorical variables. Data visualization included graphical methods such as histograms, box-and-whisker plots, and column charts for continuous data, as well as bar charts and pie charts for categorical data, as appropriate. Group comparisons for continuous variables were performed using the independent sample t-test for normally distributed data, while the Wilcoxon test was applied as a non-parametric alternative for non-normally distributed data. The Chi-squared test was employed to compare categorical variables between groups, with Fisher\u0026rsquo;s Exact test used when expected frequencies in contingency tables were \u0026lt;\u0026thinsp;5 in over 25% of cells. Correlations between continuous variables were assessed using Pearson\u0026rsquo;s correlation for normally distributed data and Spearman\u0026rsquo;s correlation for non-normally distributed data. Multivariate Poisson generalized linear regression models were applied to estimate relative risks (RRs) with 95% confidence intervals (CIs) and identify independent meteorological factors associated with the occurrence of intracerebral haemorrhage (ICH) or acute ischemic stroke (AIS). A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study was conducted over a period of 25 months from January 2020 to January 2022. A total of 1144 patients were included in the study. The baseline characteristics of the patients are given in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the study group was 61.81 (SD 13.22) years. 605 (52.9%) of the participants had haemorrhagic stroke whereas 540 (47.2%) of the participants had ischemic stroke. Overall strokes were most common in the month of January and in the winter season. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e gives the distribution of average monthly meteorological variables in haemorrhagic and ischemic strokes of our study.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of all stroke patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical Details\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber (Percentages)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e684 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e460 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e863 (75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior History of Hypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e883 (77.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompliance to Antihypertensive Drugs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e413 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegular BP Monitoring\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious Diabetic History\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompliance to Anti-Diabetic Treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157 (86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegular Monitoring of Diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious History of AF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious Anticoagulation Use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Anticoagulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVitamin K Antagonists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (44.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNovel Oral Anticoagulants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (55.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompliance to Anticoagulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (36.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInadequate Dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePast Stroke: Ischemic Stroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePast Stroke: Haemorrhagic Stroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive analysis of monthly stroke occurrence and average monthly meteorological factors.\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMonth\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Strokes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Min Temp (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Max Temp\u003c/p\u003e\n \u003cp\u003e(\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRH (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSLP\u003c/p\u003e\n \u003cp\u003e(hPa)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWindspeed\u003c/p\u003e\n \u003cp\u003e(kmph)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eJanuary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.78\u0026thinsp;\u0026plusmn;\u0026thinsp;16.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFebruary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.05\u0026thinsp;\u0026plusmn;\u0026thinsp;19.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarch\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.20\u0026thinsp;\u0026plusmn;\u0026thinsp;24.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e841.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eApril\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.68\u0026thinsp;\u0026plusmn;\u0026thinsp;25.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e840.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.70\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e839.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eJune\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.10\u0026thinsp;\u0026plusmn;\u0026thinsp;16.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e835.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eJuly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAugust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.09\u0026thinsp;\u0026plusmn;\u0026thinsp;23.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeptember\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.65\u0026thinsp;\u0026plusmn;\u0026thinsp;13.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e836.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOctober\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.91\u0026thinsp;\u0026plusmn;\u0026thinsp;14.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e840.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNovember\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.09\u0026thinsp;\u0026plusmn;\u0026thinsp;13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecember\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eICH: Intracranial Hemorrhage; AIS: Acute Ischemic Stroke; RH: Relative Humidity; SLP: Sea-level Pressure.\u003c/p\u003e\n\u003cp\u003ePutamen was the most common site of haemorrhagic stroke followed by thalamus and lobar haemorrhages. 56.3% of haemorrhages were on the left side, 42.3% on the right side and 1.4% bilateral. The mean volume (SD) was 28.23 (26.84) ml. The median volume (IQR) was 20.00 (10\u0026ndash;35) ml and the range was 1\u0026ndash;200ml. 35.8% (n\u0026thinsp;=\u0026thinsp;216) had intraventricular extension. The majority of the patients had a lower ICH score placing them in a good prognostic category group. Most of the patients had hypertensive haemorrhages. The Box-and-Whisker plot (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) depicts the distribution of the volume of ICH (ml) in each month. There was no significant association between the volume of haemorrhage and the month of the year (\u0026chi;2\u0026thinsp;=\u0026thinsp;17.055, p\u0026thinsp;=\u0026thinsp;0.106). The density plot (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) depicts the distribution of ICH Score in the 12 different months of the year. There was no significant difference between the months in terms of ICH Score (\u0026chi;2\u0026thinsp;=\u0026thinsp;18.250, p\u0026thinsp;=\u0026thinsp;0.076). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e summarize the regression analysis for haemorrhagic strokes using all the predictor variables together in one go. The \u0026apos;OR (univariable)\u0026apos; column lists the odds ratios for each of the variables with respect to the haemorrhagic stroke when these variables are used as single predictors of the dependent variable, without entering the rest of the variables in the model. The \u0026apos;OR (multivariable)\u0026apos; column lists the odds ratios for all the variables when they are entered in the model together (and are now thus controlling for each other).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and Multivariate analysis of various weather variables and haemorrhagic stroke.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHaemorrhagic Stroke\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (univariable)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (multivariable)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum Temp (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.99\u0026ndash;1.02, p\u0026thinsp;=\u0026thinsp;0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.96\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.863)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum Temp (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.4 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (1.00-1.02, p\u0026thinsp;=\u0026thinsp;0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.98\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.476)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelative Humidity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.7 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.99-1.00, p\u0026thinsp;=\u0026thinsp;0.396)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.99-1.00, p\u0026thinsp;=\u0026thinsp;0.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWindspeed (Km/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (1.12\u0026ndash;1.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.12\u0026ndash;1.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLP (HPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e839.9 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.99\u0026ndash;1.03, p\u0026thinsp;=\u0026thinsp;0.432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (1.00-1.08, p\u0026thinsp;=\u0026thinsp;0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eAmongst the ischemic stroke subtypes (TOAST Classification), Cardio embolic strokes were the most common followed by strokes of undetermined aetiology. Figure in Supplementary file 1 shows distribution of various ischemic stroke subtypes in different months of the year. Cardio embolic strokes were most common in the month of December while artery to artery strokes were most common in August and small artery strokes in April. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarize the regression analysis for ischemic strokes using all the predictor variables together in one go. The \u0026apos;OR (univariable)\u0026apos; column lists the odds ratios for each of the variables with respect to the ischemic stroke, when these variables are used as single predictors of the dependent variable, without entering the rest of the variables in the model. The \u0026apos;OR (multivariable)\u0026apos; column lists the odds ratios for all the variables when they are entered into the model together (and are now thus controlling for each other).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and Multivariate analysis of various weather variables and ischemic stroke.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIschemic Stroke\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (univariable)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (multivariable)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum Temp (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.98\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.96\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.860)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum Temp (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.4 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.98-1.00, p\u0026thinsp;=\u0026thinsp;0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.96\u0026ndash;1.02, p\u0026thinsp;=\u0026thinsp;0.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelative Humidity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.7 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (1.00-1.01, p\u0026thinsp;=\u0026thinsp;0.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (1.00-1.01, p\u0026thinsp;=\u0026thinsp;0.432)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWindspeed (Km/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 (0.76\u0026ndash;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.74\u0026ndash;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLP (HPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e839.7 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.97\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.92-1.00, p\u0026thinsp;=\u0026thinsp;0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNon-traditional risk factors have gained increasing attention in the context of stroke risk, particularly as multiple studies indicate that a significant proportion of strokes cannot be attributed solely to traditional risk factors such as hypertension, diabetes mellitus, and heart disease. Furthermore, non-traditional risk factors are known to exacerbate traditional ones, underscoring the importance of their evaluation and management. For instance, research has demonstrated that cold weather can elevate blood pressure, primarily through vasoconstriction, thereby increasing stroke risk (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, the relationship between cold weather and stroke extends beyond blood pressure, involving other mechanisms such as effects on coagulation pathways and endothelial function (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). These combined effects may significantly amplify stroke risk during colder seasons. Advising high-risk individuals to relocate to warmer areas during the winter months could be a practical recommendation, particularly in regions like the Kashmir Valley, where winters are harsh, prolonged, and frequently marked by sub-zero temperatures. Implementing such measures could help mitigate the elevated stroke risk associated with cold climates.\u003c/p\u003e \u003cp\u003eMany risk factors, such as hypertension, are shared between haemorrhagic and ischemic strokes. However, certain risk factors are specific to each subtype, which suggests that the influence of weather and its elements may not be uniform across all stroke types. In our study, we examined the relationship between stroke and four weather elements: temperature, atmospheric pressure, humidity, and wind speed. The effect of temperature on stroke risk remains a topic of debate. While most studies suggest that lower temperatures are associated with an increased stroke risk, some have reported no such relationship, and others have observed differing impacts on haemorrhagic and ischemic strokes (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In our study, neither high nor low temperatures were found to significantly increase the risk of either ischemic or haemorrhagic stroke. The reasons why lower temperatures may elevate stroke risk, as shown in many studies, are not entirely understood. Some researchers suggest that cold-induced vasoconstriction raises blood pressure, thereby increasing stroke risk, while others propose that heightened blood coagulability during winter could play a role. Additionally, colder seasons often lead to reduced physical activity, as people tend to stay indoors. However, the increased stroke risk observed during winter may not be solely attributable to lower temperatures. For instance, in the U.S., stroke mortality rates are highest in the Southeast, a region warmer than much of the country but characterized by lower physical activity levels, higher obesity rates, and increased smoking prevalence (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This highlights that temperature alone does not fully explain the elevated stroke risk observed during colder seasons. Other environmental and behavioural factors likely contribute to the seasonal variation in stroke incidence.\u003c/p\u003e \u003cp\u003eRelative humidity (RH) measures the amount of water vapor in the air relative to the maximum amount the air can hold at a given temperature. Warmer air holds more water vapor, while cooler air retains less, which explains why summers are typically hot and humid, whereas winters tend to be cold and dry. Consequently, the summer season generally experiences higher humidity levels than other seasons. The impact of relative humidity on stroke risk remains a subject of debate. Some studies have reported a significant association between relative humidity and stroke risk, while others have produced contradictory findings (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In our study, no significant association was observed between relative humidity and the risk of either hemorrhagic or ischemic stroke. High humidity, however, can lead to dehydration and heat stress, potentially impairing blood pressure regulation and vascular function (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These physiological stresses might contribute to an increased stroke risk under certain conditions. The relationship between relative humidity and stroke is influenced by multiple factors, including physiological responses, seasonal variations, and broader environmental conditions. While some evidence supports a connection between humidity and stroke risk, further research is required to elucidate the underlying mechanisms and establish clearer links.\u003c/p\u003e \u003cp\u003eWind speed, another weather parameter examined in our study, arises from the movement of air between areas of high and low pressure, primarily influenced by temperature changes. The relationship between wind speed and stroke has garnered increasing attention in epidemiological research. While the direct effects of wind speed on stroke are not well-documented, several factors suggest a potential link (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Higher wind speeds can affect the dispersion of air pollutants, and exposure to poor air quality has been associated with an increased risk of stroke. Sudden changes in weather, including shifts in wind speed, may lead to vascular instability. Research also indicates that strong winds could trigger acute cardiovascular events, including strokes. In our study, we observed a significant association between wind speed and stroke risk. Specifically, higher wind speeds were linked to an increased risk of hemorrhagic stroke and a decreased risk of ischemic stroke. The underlying reasons for this contrasting effect remain unclear. Elevated wind speeds have been shown to increase blood pressure, which may act as a precipitating factor for hypertension-related hemorrhagic strokes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWind conditions also influence outdoor physical activity levels. Reduced activity during adverse weather may contribute to stroke risk factors such as obesity and hypertension (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Additionally, windy conditions may heighten psychological stress, a recognized risk factor for stroke. Stress can elevate blood pressure and heart rate, further increasing stroke risk. Atmospheric pressure, or air pressure, is the force exerted by the weight of air on a surface within the atmosphere. It significantly impacts weather patterns, with high-pressure systems often bringing clear skies and calm conditions, while low-pressure systems are linked to clouds, precipitation, and storms. Additionally, atmospheric pressure affects environmental processes such as wind patterns, ocean currents, and pollutant dispersion (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The relationship between atmospheric pressure and stroke has been investigated in numerous studies. Research suggests that individuals living at high altitudes, where atmospheric pressure is lower, may have a reduced risk of stroke. This could be attributed to lower hypertension rates and increased physical activity at higher elevations (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, these findings are not universally consistent. Several studies indicate that reduced atmospheric pressure may increase stroke risk, particularly in individuals with pre-existing health conditions, possibly due to lower oxygen levels (hypoxia) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Furthermore, fluctuations in barometric pressure have been associated with a rise in stroke events, likely through their effects on cerebral blood flow and vascular function (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In our study, we observed that higher atmospheric pressure was linked to an increased risk of haemorrhagic stroke and a decreased risk of ischemic stroke. This suggests that the relationship between atmospheric pressure and stroke risk is multifaceted and may be influenced by various environmental factors.\u003c/p\u003e"},{"header":"Summary","content":"\u003cp\u003eThe relationship between weather and stroke risk has been a subject of extensive research, revealing several ways in which environmental factors can influence stroke occurrence. The relationship between stroke risk and individual weather parameters is complex and not entirely clear. Weather encompasses a variety of parameters that describe the state of the atmosphere at a given time and place. Thus as a whole weather may play a significant impact on stroke risk. Cold weather is particularly associated with elevated stroke risk and this is particularly relevant in Kashmir Valley where winters can be harsh. Advising people at high propensity of developing a stroke to move to warmer areas of the country during winter seasons can be a form of health education as well as a component of state health care policy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.W: Conceptualization, Methodology: W.D: Data curation, Writing- Original draft preparation. A.Y, Z.K, F.M: Visualization, Investigation. R.A, Z.P: Supervision.: I.B, A.C: Software, Validation. A.R: Reviewing and Editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG, et al. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021 Oct 1;20(10):795\u0026ndash;820. \u003c/li\u003e\n\u003cli\u003eFeigin VL, Brainin M, Norrving B, Martins SO, Pandian J, Lindsay P, et al. World Stroke Organization: Global Stroke Fact Sheet 2025. Int J Stroke. 2025 Feb;20(2):132-144. \u003c/li\u003e\n\u003cli\u003eJones SP, Baqai K, Clegg A, Georgiou R, Harris C, Holland EJ, et al. Stroke in India: A systematic review of the incidence, prevalence, and case fatality. Int J Stroke. 2022 Feb 1;17(2):132\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eShah PA, Bardi GH, Naiku BA, Dar AK, Kaul RK. Clinico-radiological profile of strokes in Kashmir valley, North-West India: A study from a university hospital. Neurol Asia. 2012;7. \u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Donnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. The Lancet. 2016 Aug 20;388(10046):761\u0026ndash;75. \u003c/li\u003e\n\u003cli\u003eTong Y, Chen Y, Yu Y, Wang F, Lin L, He G, et al. Study on the relationship among typhoon, weather change and acute ischemic stroke in southern Zhejiang Province of China. BMC Neurol. 2025 Jan 8;25(1):14. \u003c/li\u003e\n\u003cli\u003eAdams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993 Jan;24(1):35\u0026ndash;41. \u003c/li\u003e\n\u003cli\u003eMinistry of Earth Sciences. India Meteorological Department, Met Centre Srinagar. Jammu \u0026amp; Kashmir. Available from: https://mausam.imd.gov.in/srinagar/\u003c/li\u003e\n\u003cli\u003ePark S, Kario K, Chia YC, Turana Y, Chen CH, Buranakitjaroen P, et al; HOPE Asia Network. The influence of the ambient temperature on blood pressure and how it will affect the epidemiology of hypertension in Asia. J Clin Hypertens (Greenwich). 2020 Mar;22(3):438-444.\u003c/li\u003e\n\u003cli\u003eLichtman JH, Leifheit-Limson EC, Jones SB, Wang Y, Goldstein LB. Average Temperature, Diurnal Temperature Variation, and Stroke Hospitalizations. J Stroke Cerebrovasc Dis. 2016 Jun 1;25(6):1489\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eDanh N, Ho C, Ford E, Zhang J, Hong H, Reid C, et al. Association between ambient temperature and stroke risk in high-risk populations: a systematic review. Front Neurol. 2024;14. \u003c/li\u003e\n\u003cli\u003eChen R, Wang C, Meng X, Chen H, Thach TQ, Wong CM, et al. Both low and high temperature may increase the risk of stroke mortality. Neurology. 2013 Sep 17;81(12):1064\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eSung FC, Yip HT, Lin CL, Jeng JS, Lee JT, Sun Y, et al. 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Stroke \u003c/em\u003e2:1223255. \u003c/li\u003e\n\u003cli\u003eZheng B, Luo Y, Li Y, Gu G, Jiang J, Chen C, et al. Prevalence and risk factors of stroke in high-altitude areas: a systematic review and meta-analysis. BMJ Open. 2023 Sep 1;13(9):e071433. \u003c/li\u003e\n\u003cli\u003eHonig A, Eliahou R, Pikkel YY, Leker RR. Drops in Barometric Pressure Are Associated with Deep Intracerebral Hemorrhage. J Stroke Cerebrovasc Dis. 2016 Apr 1;25(4):872\u0026ndash;6. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-neurology-psychiatry-and-neurosurgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejnp","sideBox":"Learn more about [The Egyptian Journal of Neurology, Psychiatry and Neurosurgery](http://ejnpn.springeropen.com)","snPcode":"41983","submissionUrl":"https://submission.springernature.com/new-submission/41983/3","title":"The Egyptian Journal of Neurology, Psychiatry and Neurosurgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Stroke, Ischemic stroke, Hemorrhagic stroke, Weather, Meteorological factors, Kashmir Valley","lastPublishedDoi":"10.21203/rs.3.rs-7727415/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7727415/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Stroke is a leading cause of mortality and disability worldwide. While traditional risk factors are well-established, the role of meteorological parameters like temperature, humidity, atmospheric pressure, and wind speed remains unclear. This study investigates the relationship between acute ischemic stroke (AIS) and intracerebral hemorrhage (ICH) with weather in the Kashmir Valley, known for its extreme winters. A 25-month prospective study (January 2020–January 2022) included 1,144 stroke patients admitted to a tertiary care center. Strokes were classified as ischemic or hemorrhagic based on CT imaging. Meteorological data for stroke onset days were retrieved from the Indian Meteorological Department. Associations between weather variables and stroke subtypes were analyzed using multivariate regression models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Of 1,144 patients, 52.9% (605) had ICH, and 47.2% (540) had AIS. Stroke incidence peaked in winter, especially in January. Higher atmospheric pressure and wind speed were associated with increased ICH risk but reduced AIS risk. Temperature and humidity had no significant effect on either subtype. Hypertension was the leading cause of ICH, with the putamen most affected. Cardio-embolic strokes were the predominant AIS subtype, showing seasonal variation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Meteorological factors, particularly atmospheric pressure and wind speed, influence stroke risks differently for AIS and ICH. Extreme weather conditions may increase stroke risk, especially for hemorrhagic strokes. Public health strategies, such as advising at-risk individuals to limit exposure to harsh winters, could reduce stroke incidence in regions with extreme climates.\u003c/p\u003e","manuscriptTitle":"Relationship of Acute Ischemic and Hemorrhagic Stroke with Weather and its Parameters.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-05 06:17:39","doi":"10.21203/rs.3.rs-7727415/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T08:24:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-29T06:42:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206739584631816256074654802114841088482","date":"2026-01-21T17:49:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-02T09:41:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T04:54:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Neurology, Psychiatry and Neurosurgery","date":"2025-12-30T06:47:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-neurology-psychiatry-and-neurosurgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejnp","sideBox":"Learn more about [The Egyptian Journal of Neurology, Psychiatry and Neurosurgery](http://ejnpn.springeropen.com)","snPcode":"41983","submissionUrl":"https://submission.springernature.com/new-submission/41983/3","title":"The Egyptian Journal of Neurology, Psychiatry and Neurosurgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fc9aa11a-418d-4ba8-b46d-4dc6efa3a442","owner":[],"postedDate":"January 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T08:39:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-05 06:17:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7727415","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7727415","identity":"rs-7727415","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}