When the Weather Turns Cold: Seasonal Patterns of Subarachnoid Hemorrhage in a Tropical Country

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Seasonal patterns have been documented in temperate regions, but evidence from tropical and subtropical populations remains limited. Objectives We investigated associations between meteorological variables and SAH hospital admissions in Brazil across diverse climate zones. Methods We conducted an ecological time-series analysis linking nationwide SAH hospitalizations (ICD-10: I60.0–I60.9) from Brazil's Unified Health System with meteorological data from the National Institute of Meteorology, 2020–2024. Generalized additive models with distributed lag structures were used to assess the relationships between temperature, humidity, atmospheric pressure, and monthly SAH incidence. Air pollution analysis was attempted but revealed critical surveillance gaps. Stratified analyses explored effect modification by season, age, and region. Results Among 3,323 SAH admissions, incidence peaked during autumn (26.4%) and winter (25.4%) when mean temperatures were 3.4°C lower than summer. Temperature showed inverse associations with admissions in autumn (β = −8.3 per °C; 95% CI: −11.2, − 5.4) and winter (β = −7.1 per °C; 95% CI: −9.8, − 4.4). Effects were stronger in southern regions and among individuals aged 65 years or older. Air quality data were available for only three months, which precluded analysis and highlighted infrastructure deficiencies. Discussion SAH admissions in Brazil increase during colder months with a dose-response relationship. Findings align with cold-induced sympathetic activation and blood pressure elevation. The absence of integrated air pollution monitoring represents a major surveillance gap. Results support the incorporation of meteorological indicators into neurocritical care planning and underscore the urgent need for an environmental health data infrastructure in Latin America. Subarachnoid Hemorrhage Climate Change Air Pollution Health Inequalities Weather Latin America Figures Figure 1 Figure 2 Figure 3 Introduction Subarachnoid hemorrhage (SAH) is a catastrophic cerebrovascular event accounting for approximately 5% of all strokes worldwide, with case fatality rates around 35% and long-term disability affecting up to half of survivors (1–3). Beyond established risk factors such as hypertension, smoking, and aneurysm characteristics, emerging evidence suggests that environmental factors—particularly meteorological variables—may modulate SAH risk through both physiological and behavioral pathways (4–6). Temperature fluctuations induce sympathetic nervous system activation, peripheral vasoconstriction, and blood pressure elevation that may destabilize aneurysmal walls (7). Cold exposure specifically triggers noradrenergic-mediated cutaneous vasoconstriction, increasing systemic vascular resistance and arterial pressure (8,9). Additionally, cold temperatures promote prothrombotic changes, including elevated fibrinogen levels, increased platelet aggregation, and higher blood viscosity (10). These hemodynamic and hemostatic alterations create conditions conducive to aneurysm rupture, particularly in individuals with pre-existing hypertension or unruptured aneurysms. International studies consistently demonstrate seasonal SAH patterns, with peak incidence during colder months in temperate climates (11–13). A European meta-analysis of 18,714 patients found a 0.4% increase in risk per 1°C decrease in temperature (14). Similar associations have been reported in Asian cohorts, particularly among younger populations (15). However, evidence from tropical and subtropical regions remains limited, leaving uncertainty about whether moderate thermal variations in warmer climates exert comparable effects. Air pollution represents another potential environmental trigger (16,17). Particulate matter exposure induces systemic inflammation, endothelial dysfunction, and autonomic imbalance—mechanisms that could compromise vascular integrity and precipitate rupture (18,19). Some studies suggest temporally lagged associations between PM₂.₅ peaks and SAH occurrence (18–20), although findings remain heterogeneous, and data from low- and middle-income countries are notably sparse. The climate-SAH relationship likely involves complex interactions between physiological responses and behavioral adaptations (21,22). While seasonal blood pressure elevations during winter months are well-documented, ranging from 3 to 30 mmHg in population studies (23,24), behavioral contributions remain less certain. Smoking patterns exhibit seasonal variability, with outdoor smoking decreasing in cold weather but indoor exposure potentially increasing due to reduced ventilation, especially in lower-income households (25,26). These behavioral pathways require further investigation with direct observational data. Brazil provides a unique epidemiological context for examining environmental determinants of SAH (27). The country encompasses diverse climate zones—from equatorial in the north to subtropical in the south—with marked seasonal temperature variations in the southern regions (28,29). Brazil's Unified Health System (SUS) serves approximately 75% of the population, providing comprehensive hospitalization data, yet significant regional disparities persist in healthcare access and stroke outcomes (30–32). Critically, environmental health surveillance infrastructure remains underdeveloped, with fragmented air quality monitoring that limits comprehensive risk assessment. This study examined the relationships between meteorological variables and SAH hospital admissions across Brazil from 2020 to 2024, employing an ecological time-series design with nationwide hospitalization data. We examined seasonal patterns, temperature-related trends, and attempted to analyze air pollution to characterize environmental risk factors in a middle-income country with substantial climate diversity. Given the limitations encountered in accessing air quality data, this work also highlights critical gaps in environmental health surveillance infrastructure that impede evidence-based public health planning in Latin America. Study Design and Data Sources We conducted an ecological time-series analysis of nationwide hospitalizations for subarachnoid hemorrhage (SAH) in Brazil from January 2020 to December 2024, examining associations with meteorological variables. Hospitalization Data SAH admissions (International Classification of Diseases, 10th Revision [ICD-10] codes I60.0–I60.9) were obtained from Brazil's Department of Informatics of the Unified Health System (DATASUS) (33), which captures all public hospital admissions representing approximately 75% of the Brazilian population. We extracted monthly admission counts, including patient age, sex, and state of residence. Population denominators for incidence rate calculations came from the Brazilian Institute of Geography and Statistics (IBGE) (34). Data completeness was verified by checking for implausible values and temporal discontinuities; no substantial gaps were identified. Meteorological Data Climate variables were obtained from the National Institute of Meteorology (INMET) (35), Brazil's official meteorological service. We extracted monthly averages of maximum and minimum temperature (°C), relative humidity (%), atmospheric pressure (hPa), and total precipitation (mm) from all operational weather stations nationwide. Complete spatial coverage with adequate station density across Brazil's five geographic regions became available only from 2020 onward; therefore, our analysis focused on this five-year period, despite the availability of hospitalization records dating back to 2015. Station-level data were aggregated to state-level monthly means weighted by population distribution using inverse distance weighting. Outliers exceeding 3 standard deviations from the station-specific long-term mean were flagged and removed after manual verification (affecting < 0.5% of observations). Missing values (< 2% of station-months) were imputed using linear interpolation between adjacent months within the same station. For stations with gaps exceeding 2 consecutive months, we used spatial interpolation from neighboring stations within a 200 km radius. Air Pollution Data and Surveillance Infrastructure Limitations We attempted to incorporate air quality data from OpenAQ (36), a global, open-access platform that aggregates real-time measurements from monitoring stations. However, systematic nationwide particulate matter monitoring data were unavailable for Brazil during the study period. Only three months of PM₁₀ measurements from selected urban monitoring stations in 2023 could be retrieved (April–June 2023, covering São Paulo, Rio de Janeiro, and Brasília), representing < 5% of the study period and < 20% of the national population. These limited data were aggregated to monthly means and are presented descriptively (Table 1 ) to document critical deficiencies in environmental health surveillance infrastructure; however, they were not included in statistical models due to insufficient temporal and spatial coverage for meaningful inference. This data gap reflects broader challenges in Brazil's environmental monitoring capacity and represents a major limitation for integrated environmental health risk assessment in Latin America. Statistical Analysis Primary Models We used generalized additive models (GAMs) with quasi-Poisson distributions to assess associations between meteorological variables and monthly SAH incidence, accounting for overdispersion commonly observed in count data (37). The base model structure incorporated a penalized cubic regression spline for long-term temporal trends (7 degrees of freedom per year of observation), cyclic cubic splines for seasonal patterns (12 knots), state-specific effects to account for regional baseline differences, and a population offset term (log-transformed) Meteorological exposures (temperature, humidity, atmospheric pressure, precipitation) were modeled as smooth terms using thin-plate regression splines. To assess potential delayed effects, we implemented distributed lag non-linear models (DLNMs) using cross-basis functions that simultaneously model non-linear exposure-response relationships and lagged effects up to 3 months (37). The temperature-lag cross-basis function utilized natural cubic splines with three internal knots placed at the 10th, 50th, and 90th percentiles of the temperature distribution, and a natural cubic spline for the lag dimension with three equally spaced knots. All continuous meteorological variables were standardized (z-scores: [x - mean]/SD) to enable direct comparison of effect sizes across different measurement scales. Seasonal Stratification We examined effect modification by season according to Southern Hemisphere meteorological definitions: summer (December–February), autumn (March–May), winter (June–August), and spring (September–November). Season-specific linear regression models assessed temperature-SAH associations within each period: SAH admissions = β₀ + β₁·mean temperature + β₂·temperature range + ε where β₁ represents the change in monthly admissions per 1°C increase in mean temperature, and β₂ captures the effect of diurnal temperature variation (maximum - minimum). Models were adjusted for precipitation and humidity as potential confounders. Subgroup Analyses Stratified analyses were performed to identify vulnerable populations and regional variations, age group: <65 years vs. ≥65 years (assessing thermoregulatory vulnerability in the elderly), sex: male vs. female (exploring potential differences), geographic region: North (equatorial/tropical), Northeast (tropical/semi-arid), Central-West (tropical savanna), Southeast (tropical/subtropical transition), and South (subtropical, coldest winters), relative risks (RR) and 95% confidence intervals (CI) were estimated for each stratum, with interaction terms tested using likelihood ratio tests (p < 0.05 considered significant), Sensitivity Analyses Model robustness was evaluated through, varying degrees of freedom for time trends (5–10 df/year), Testing alternative lag periods (0–6 months), Excluding the first and last 6 months of observations to evaluate boundary artifacts, Comparing quasi-Poisson with, negative binomial regression models, Testing models with and without correlated variables (e.g., removing humidity/precipitation when examining temperature), re-running analyses after excluding months with extreme SAH counts (> 2 SD from mean) Model Diagnostics We assessed model adequacy through Examination of deviance residuals for temporal patterns and heteroscedasticity, Partial autocorrelation function (PACF) plots to detect residual serial correlation (up to 12 lags), Quantile-quantile (Q-Q) plots comparing observed vs. theoretical residual distributions, Mean absolute percentage error (MAPE) between observed and fitted values, Variance inflation factors (VIF 1 indicates overdispersion) Software and Reproducibility All analyses were performed in Python version 3.11 using statsmodels (v0.14), scipy (v1.11), pandas (v2.0), and pygam (v0.8) libraries. Graphical outputs were generated using matplotlib (version 3.7) and seaborn (version 0.12). Complete analysis code and aggregated data are available from the corresponding author upon reasonable request. Individual-level data cannot be shared due to data protection regulations, but aggregated monthly counts by state, age group, and sex are available. Ethical Considerations This study utilized anonymized, publicly available aggregate data that precluded individual identification. The study was exempt from institutional review board approval under Brazilian National Health Council Resolution 510/2016, which exempts research using de-identified public databases. No individual consent was required, given the use of aggregate administrative data. Results Between January 2020 and December 2024, a total of 3,323 hospital admissions for subarachnoid hemorrhage (SAH) were recorded in Brazil's public health system. Mean monthly admissions were 55.4 (SD = 12.3, range: 28–89). The highest seasonal incidence occurred in autumn (26.4%, n = 877) and winter (25.4%, n = 844), compared to spring (23.9%, n = 794) and summer (24.3%, n = 808). The mean age at admission was 54.7 years (SD = 13.2), with 58.3% of patients being female. Regional distribution showed the highest admission rates in the Southeast (42.1%) and South (28.6%) regions. Temporal and Seasonal Patterns Monthly time-series analysis revealed consistent peaks in SAH admissions during colder months, particularly from May to July (Fig. 1 ). Seasonal temperature patterns showed marked differences: the mean winter temperature (22.2°C, SD = 2.8°C) was 3.4°C lower than the summer temperature (25.6°C, SD = 2.1°C). The temporal correspondence between lower temperatures and higher SAH incidence is visually apparent throughout the study period (Fig. 1 ). Temperature-SAH Associations Season-stratified linear regression analyses demonstrated significant negative associations between temperature and SAH admissions in autumn (β = −8.3 admissions per °C; 95% CI: −11.2, − 5.4; p < 0.001) and winter (β = −7.1 per °C; 95% CI: −9.8, − 4.4; p < 0.001). In spring, the association was weaker and marginally significant (β = −3.2 per °C; 95% CI: −6.8, 0.4; p = 0.08), while summer showed no meaningful relationship (β = −0.4 per °C; 95% CI: −3.1, 2.3; p = 0.64) (Fig. 2 ). Standardized analyses confirmed the inverse temperature-SAH relationship, with correlation coefficients of r = − 0.42 (p = 0.02) for maximum temperature and r = − 0.38 (p = 0.04) for minimum temperature (Fig. 3 ). The quasi-Poisson GAM showed adequate fit (dispersion parameter φ = 1.32, indicating moderate overdispersion), with no substantial residual autocorrelation (PACF < 0.15 for all lags). Distributed Lag Analysis DLNM analysis revealed that the strongest temperature effect occurred within the same month (lag 0), with a relative risk of 1.18 (95% CI: 1.09–1.28) per 1°C decrease below the median temperature. Cumulative effects persisted for up to 2 months, though effect sizes diminished at longer lags. No significant non-linear temperature effects were detected beyond the linear relationship. Subgroup Analyses Stronger temperature-SAH associations were observed in the South region (β = −12.4 per °C; 95% CI: −17.8, − 7.0; p < 0.001) compared to other regions (p for interaction = 0.03). Age-stratified analysis showed amplified effects among individuals ≥ 65 years (RR = 1.24 per 1°C decrease; 95% CI: 1.12, 1.37) compared to younger adults (RR = 1.11; 95% CI: 1.03, 1.20; p for interaction = 0.04). No significant sex differences were detected (p = 0.42). Air Pollution Data Limitations Air quality data were severely limited. Only three months of PM₁₀ measurements from urban stations were available (April–June 2023), precluding time-series analysis (Table 1 ). These sparse data are presented to document surveillance infrastructure deficiencies rather than to draw epidemiological inferences. Discussion This five-year nationwide ecological analysis reveals a clear inverse relationship between ambient temperature and subarachnoid hemorrhage (SAH) hospital admissions in Brazil, with a peak incidence during the colder autumn and winter months. These findings extend evidence from temperate regions (8,9,11,15) to tropical and subtropical settings, demonstrating that even moderate seasonal temperature variations (a mean difference of 3.4°C between winter and summer) significantly influence SAH risk. The results support the hypothesis that environmental factors modulate cerebrovascular vulnerability beyond established individual risk factors (4,19). The observed temperature-SAH association is biologically plausible through well-established physiological pathways. Cold exposure triggers sympathetic nervous system activation, leading to peripheral vasoconstriction and increased systemic vascular resistance, which in turn elevates arterial blood pressure and may destabilize aneurysmal walls (7,38). Cold temperatures also promote prothrombotic changes, including elevated fibrinogen levels, increased platelet aggregation, and higher blood viscosity (10), creating conditions conducive to aneurysm rupture. These mechanisms, extensively documented in European and Asian populations (11,15), appear to be consistent with the patterns observed in our Brazilian cohort. The stronger associations in southern Brazil, where winters are more pronounced, and among elderly individuals (≥ 65 years), who have diminished thermoregulatory capacity, support the cold-exposure hypothesis. The immediate effect (lag 0) and short persistence (up to 2 months) align with acute hemodynamic responses rather than cumulative exposures. The near-complete absence of air pollution data represents a critical limitation—not primarily of this study, but of Brazil's environmental health surveillance infrastructure. Despite evidence from other countries linking particulate matter exposure to cerebrovascular events through systemic inflammation, endothelial dysfunction, and autonomic imbalance (13,18–20), we were unable to evaluate this pathway. Only three months of PM₁₀ data were retrievable, exemplifying broader challenges in environmental health monitoring across Latin America, where integrated air quality surveillance remains fragmented and geographically limited (Table 1 ). This surveillance gap has substantial implications beyond research limitations. The inability to assess synergistic effects between temperature and air pollution on SAH risk represents a missed opportunity for comprehensive environmental health risk assessment. Future studies integrating long-term, high-resolution air quality data with health outcomes are essential, but first require investment in monitoring infrastructure. These findings have important implications for neurocritical care planning in Brazil. The consistent seasonal pattern suggests opportunities for targeted capacity planning, with neurocritical care resources accounting for predictable autumn/winter surges in SAH admissions, particularly in southern states where temperature effects are most pronounced (39). Integrating meteorological data—including temperature drops, atmospheric pressure changes, and humidity shifts—into public health surveillance could enable proactive capacity adjustments (40,41). Such environmental early warning systems could alert healthcare facilities to periods of elevated SAH risk, facilitating the preparation of staffing and resources before seasonal peaks (41,42). Climate change adds further complexity to this scenario. Increasing temperature variability and extreme weather events may amplify seasonal disease burdens (14,24,40). While long-term warming might reduce winter-associated peaks in some areas, increased climate volatility and abrupt temperature transitions could trigger acute hemodynamic stresses in vulnerable populations (43,44). Given Brazil's regional heterogeneity, climate change impacts on SAH incidence are likely to be uneven, potentially exacerbating existing health inequalities between northern tropical and southern subtropical regions (40). This study has inherent limitations. The ecological design precludes individual-level causal inference and cannot determine whether specific patients were exposed to cold immediately before rupture. Data were restricted to public system admissions (~ 75% of the population), potentially missing privately insured patients with different risk profiles. We lacked individual-level confounders (hypertension control, aneurysm characteristics, medications). Monthly aggregation may obscure the acute effects of temperature fluctuations. Air pollution analysis was impossible due to infrastructure deficiencies. The five-year period, while capturing robust seasonal patterns, may not reflect long-term climate trends. Despite these constraints, this nationwide analysis provides novel evidence that moderate climatic fluctuations meaningfully influence SAH occurrence in a climatically diverse middle-income country, with implications for environmental health surveillance and neurocritical care preparedness across Latin America. Conclusion SAH admissions in Brazil increase during colder months, with a consistent inverse association between temperature and incidence. This pattern is biologically plausible, reflecting cold-induced sympathetic activation, peripheral vasoconstriction, and prothrombotic changes. These findings extend evidence from temperate regions to tropical and subtropical settings, demonstrating that even moderate seasonal temperature variations can meaningfully influence SAH risk in climatically diverse populations. The critical gap in air pollution monitoring infrastructure represents a significant impediment to comprehensive environmental health risk assessment in Brazil, highlighting the urgent need for investment in environmental surveillance across Latin America. Findings suggest the need to incorporate seasonal considerations into neurocritical care planning and integrate meteorological indicators into health surveillance systems. As climate variability intensifies, adaptive surveillance strategies and seasonal preparedness will be crucial in preventing disproportionate impacts on vulnerable regions and populations. Future research should prioritize establishing comprehensive air quality monitoring networks and examining individual-level interactions between environmental exposures and cerebrovascular risk. Declarations Compliance with Instructions to Authors: The authors confirm that this manuscript complies with all instructions to authors provided by the Neurocritical Care journal, including formatting, word count, figure/table specifications, reference style, and ethical requirements. General Statement: All authors have read and approved the submitted manuscript. This manuscript has not been submitted elsewhere nor published elsewhere in whole or in part. All authors confirm the integrity of the work and take responsibility for the content and conclusions presented herein. Authors' Contributions: All authors meet the ICMJE criteria for authorship and have approved the final manuscript. Individual contributions were as follows: Daniela Laranja Gomes Rodrigues (DLGR): Study conceptualization, design, data collection coordination, data analysis, manuscript drafting, and project supervision. João Brainer Clares de Andrade (JBCA): Study design, critical review of manuscript, methodological supervision, and approval of final version. Gisele Sampaio Silva (GSS): Study design, critical revision for important intellectual content, and approval of final version for submission. All authors contributed to the interpretation of data and critical revision of the manuscript. All authors approved the final version and agree to be accountable for all aspects of the work. Funding: This work has no funding. Conflicts of Interest: The authors declare no competing interests, financial or otherwise, related to the study design, execution, analysis, interpretation, writing, or decision to publish this manuscript. Ethics Approval and Informed Consent: The study utilized only aggregated, de-identified service data without individual patient identifiers. Individual informed consent was not required due to the use of non-identifiable aggregated data. No patient-level data were collected or analyzed. Use of Generative AI and AI-Assisted Technologies: During the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-4) to assist with English language editing, grammar review, and translation of Portuguese content into English. All AI-generated content was thoroughly reviewed, edited, and verified by the authors to ensure accuracy, clarity, and scientific rigor. The authors take full responsibility for the content and integrity of this publication. AI tools were not used for data analysis, interpretation, or the generation of scientific conclusions. Data Availability: Hospitalization data are publicly available from DATASUS (http://datasus.saude.gov.br/). Meteorological data are available from INMET (https://portal.inmet.gov.br/). Limited air pollution data were obtained from OpenAQ (https://openaq.org/). Aggregated analysis datasets and code are available from the corresponding author upon reasonable request, subject to data protection regulations. Guarantor: Daniela Laranja Gomes Rodrigues serves as the guarantor for this manuscript and accepts full responsibility for the work, including the study design, access to data, and the decision to submit for publication. Justification: Study conducted without external funding through institutional social responsibility initiatives No access to publication budgets or research grants Authors from a middle-income country (Brazil), where APC represents a substantial barrier Findings address critical knowledge gap with direct implications for neurocritical care planning globally Commitment to open data/code sharing to maximize impact We believe this research contributes important evidence from underrepresented populations to the neurocritical care literature. A waiver would enable maximum dissemination to clinicians and policymakers in resource-limited settings who would benefit most. Authors' Contributions All authors meet the ICMJE criteria for authorship and have approved the final manuscript. Individual contributions were as follows: Daniela Laranja Gomes Rodrigues (DLGR): Study conceptualization, design, data collection coordination, data analysis, manuscript drafting, and project supervision. 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Stat Med. 2010 Sept 20;29(21):2224–34. Yamazaki F. The cutaneous vasoconstrictor response in lower extremities during whole-body and local skin cooling in young women with a cold constitution. J Physiol Sci. 2015 Sept;65(5):397–405. Silva GS, Maldonado NJ, Mejia-Mantilla JH, Ortega-Gutierrez S, Claassen J, Varelas P, et al. Neuroemergencies in South America: How to Fill in the Gaps? Neurocrit Care. 2019;31(3):573–82. Péres WE, Ribeiro AFS, Russo A, Nunes B. The Association between Air Temperature and Mortality in Two Brazilian Health Regions. Climate. 2020 Jan 19;8(1):16. World Health Organization. Operational framework for building climate resilient health systems [Internet]. Geneva: World Health Organization; 2015 [cited 2025 Aug 18]. 47 p. Available from: https://iris.who.int/handle/10665/189951 Yarova K, Solodovnikova Y, Son A. The Role of Meteorological Factors in Cerebral Anterior versus Posterior Circulation Aneurysm Rupture. World Neurosurg. 2025 Sept;201:124269. Gasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet. 2015 July;386(9991):369–75. Zanobetti A, O’Neill MS, Gronlund CJ, Schwartz JD. Summer temperature variability and long-term survival among elderly people with chronic disease. Proc Natl Acad Sci. 2012 Apr 24;109(17):6608–13. Tables Table 1: Limited PM ₁₀ data available in Brazil (2023) Month PM ₁₀ (µg/m³) SAH Admissions April 2023 18.1 186 May 2023 20.6 204 June 2023 29.6 153 Legend. Limited PM₁₀ data available for Brazil (April-June 2023 only). Data from selected urban monitoring stations (São Paulo, Rio de Janeiro, Brasília) represent <5% of the study period and <20% of the national population. PM₁₀, particulate matter ≤10 micrometers; SAH, subarachnoid hemorrhage. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor invited by journal 16 Oct, 2025 Editor assigned by journal 14 Oct, 2025 First submitted to journal 10 Oct, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7792143","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533452959,"identity":"0b8a8290-4565-4f38-80c6-a09d70a2bcf7","order_by":0,"name":"Daniela Laranja Gomes 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16:20:14","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73129,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7792143/v1/fb9e77ebbb4485a8e3473242.html"},{"id":95038516,"identity":"8fbf61ef-17e3-43a5-b2c2-edf3acf648e1","added_by":"auto","created_at":"2025-11-03 15:47:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216495,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of monthly subarachnoid hemorrhage admissions and temperature variations in Brazil (2020-2024).\u003c/p\u003e\n\u003cp\u003eMonthly SAH admissions (blue line, left axis) and mean maximum (red) and minimum (orange) temperatures (right axis). Vertical dashed lines indicate seasonal transitions. Peak admissions occurred during May–July (autumn-winter). SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"Figure1hsaclimate.png","url":"https://assets-eu.researchsquare.com/files/rs-7792143/v1/721c66857b1c6698a8319792.png"},{"id":95038513,"identity":"0f0e762b-81ee-4722-b71d-a8d8e30f8329","added_by":"auto","created_at":"2025-11-03 15:47:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114579,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal stratification of the relationship between temperature and subarachnoid hemorrhage admissions in Brazil (2020-2024).\u003c/p\u003e\n\u003cp\u003eMonthly SAH admissions versus mean temperature by season: (A) Autumn, (B) Winter, (C) Spring, (D) Summer. Points represent observations; lines show linear fits with 95% CI (shaded). Regression coefficients and p-values are shown in panels. CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure2hsaclimate.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7792143/v1/af66ba82f099775c3635d371.jpg"},{"id":95038512,"identity":"9bb2625c-15ab-4446-a65d-1f57da772401","added_by":"auto","created_at":"2025-11-03 15:47:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61838,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized trends comparing SAH admissions and annual temperature anomalies in Brazil (2020-2024).\u003c/p\u003e\n\u003cp\u003eZ-scores for monthly SAH admissions (blue) and temperatures (maximum: red dashed; minimum: orange dashed) relative to period means. Gray bands indicate ±1 SD. Correlation: r = −0.42 (p = 0.02). SD, standard deviation.\u003c/p\u003e","description":"","filename":"Figure3hsaclimate.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7792143/v1/9c0d8e2d36b865c6f15d9b96.jpg"},{"id":95312258,"identity":"e4539038-6f6a-4e39-abec-5fc33d93f1a3","added_by":"auto","created_at":"2025-11-06 15:48:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1026142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7792143/v1/d59922ce-1c59-4795-ac15-b85e8770fb78.pdf"}],"financialInterests":"","formattedTitle":"When the Weather Turns Cold: Seasonal Patterns of Subarachnoid Hemorrhage in a Tropical Country","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSubarachnoid hemorrhage (SAH) is a catastrophic cerebrovascular event accounting for approximately 5% of all strokes worldwide, with case fatality rates around 35% and long-term disability affecting up to half of survivors (1\u0026ndash;3). Beyond established risk factors such as hypertension, smoking, and aneurysm characteristics, emerging evidence suggests that environmental factors\u0026mdash;particularly meteorological variables\u0026mdash;may modulate SAH risk through both physiological and behavioral pathways (4\u0026ndash;6).\u003c/p\u003e\u003cp\u003eTemperature fluctuations induce sympathetic nervous system activation, peripheral vasoconstriction, and blood pressure elevation that may destabilize aneurysmal walls (7). Cold exposure specifically triggers noradrenergic-mediated cutaneous vasoconstriction, increasing systemic vascular resistance and arterial pressure (8,9). Additionally, cold temperatures promote prothrombotic changes, including elevated fibrinogen levels, increased platelet aggregation, and higher blood viscosity (10). These hemodynamic and hemostatic alterations create conditions conducive to aneurysm rupture, particularly in individuals with pre-existing hypertension or unruptured aneurysms.\u003c/p\u003e\u003cp\u003eInternational studies consistently demonstrate seasonal SAH patterns, with peak incidence during colder months in temperate climates (11\u0026ndash;13). A European meta-analysis of 18,714 patients found a 0.4% increase in risk per 1\u0026deg;C decrease in temperature (14). Similar associations have been reported in Asian cohorts, particularly among younger populations (15). However, evidence from tropical and subtropical regions remains limited, leaving uncertainty about whether moderate thermal variations in warmer climates exert comparable effects.\u003c/p\u003e\u003cp\u003eAir pollution represents another potential environmental trigger (16,17). Particulate matter exposure induces systemic inflammation, endothelial dysfunction, and autonomic imbalance\u0026mdash;mechanisms that could compromise vascular integrity and precipitate rupture (18,19). Some studies suggest temporally lagged associations between PM₂.₅ peaks and SAH occurrence (18\u0026ndash;20), although findings remain heterogeneous, and data from low- and middle-income countries are notably sparse.\u003c/p\u003e\u003cp\u003eThe climate-SAH relationship likely involves complex interactions between physiological responses and behavioral adaptations (21,22). While seasonal blood pressure elevations during winter months are well-documented, ranging from 3 to 30 mmHg in population studies (23,24), behavioral contributions remain less certain. Smoking patterns exhibit seasonal variability, with outdoor smoking decreasing in cold weather but indoor exposure potentially increasing due to reduced ventilation, especially in lower-income households (25,26). These behavioral pathways require further investigation with direct observational data.\u003c/p\u003e\u003cp\u003eBrazil provides a unique epidemiological context for examining environmental determinants of SAH (27). The country encompasses diverse climate zones\u0026mdash;from equatorial in the north to subtropical in the south\u0026mdash;with marked seasonal temperature variations in the southern regions (28,29). Brazil's Unified Health System (SUS) serves approximately 75% of the population, providing comprehensive hospitalization data, yet significant regional disparities persist in healthcare access and stroke outcomes (30\u0026ndash;32). Critically, environmental health surveillance infrastructure remains underdeveloped, with fragmented air quality monitoring that limits comprehensive risk assessment.\u003c/p\u003e\u003cp\u003eThis study examined the relationships between meteorological variables and SAH hospital admissions across Brazil from 2020 to 2024, employing an ecological time-series design with nationwide hospitalization data. We examined seasonal patterns, temperature-related trends, and attempted to analyze air pollution to characterize environmental risk factors in a middle-income country with substantial climate diversity. Given the limitations encountered in accessing air quality data, this work also highlights critical gaps in environmental health surveillance infrastructure that impede evidence-based public health planning in Latin America.\u003c/p\u003e"},{"header":"Study Design and Data Sources","content":"\u003cp\u003eWe conducted an ecological time-series analysis of nationwide hospitalizations for subarachnoid hemorrhage (SAH) in Brazil from January 2020 to December 2024, examining associations with meteorological variables.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eHospitalization Data\u003c/h2\u003e\u003cp\u003eSAH admissions (International Classification of Diseases, 10th Revision [ICD-10] codes I60.0\u0026ndash;I60.9) were obtained from Brazil's Department of Informatics of the Unified Health System (DATASUS) (33), which captures all public hospital admissions representing approximately 75% of the Brazilian population. We extracted monthly admission counts, including patient age, sex, and state of residence. Population denominators for incidence rate calculations came from the Brazilian Institute of Geography and Statistics (IBGE) (34). Data completeness was verified by checking for implausible values and temporal discontinuities; no substantial gaps were identified.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeteorological Data\u003c/h3\u003e\n\u003cp\u003eClimate variables were obtained from the National Institute of Meteorology (INMET) (35), Brazil's official meteorological service. We extracted monthly averages of maximum and minimum temperature (\u0026deg;C), relative humidity (%), atmospheric pressure (hPa), and total precipitation (mm) from all operational weather stations nationwide. Complete spatial coverage with adequate station density across Brazil's five geographic regions became available only from 2020 onward; therefore, our analysis focused on this five-year period, despite the availability of hospitalization records dating back to 2015.\u003c/p\u003e\u003cp\u003eStation-level data were aggregated to state-level monthly means weighted by population distribution using inverse distance weighting. Outliers exceeding 3 standard deviations from the station-specific long-term mean were flagged and removed after manual verification (affecting\u0026thinsp;\u0026lt;\u0026thinsp;0.5% of observations). Missing values (\u0026lt;\u0026thinsp;2% of station-months) were imputed using linear interpolation between adjacent months within the same station. For stations with gaps exceeding 2 consecutive months, we used spatial interpolation from neighboring stations within a 200 km radius.\u003c/p\u003e\n\u003ch3\u003eAir Pollution Data and Surveillance Infrastructure Limitations\u003c/h3\u003e\n\u003cp\u003eWe attempted to incorporate air quality data from OpenAQ (36), a global, open-access platform that aggregates real-time measurements from monitoring stations. However, systematic nationwide particulate matter monitoring data were unavailable for Brazil during the study period. Only three months of PM₁₀ measurements from selected urban monitoring stations in 2023 could be retrieved (April\u0026ndash;June 2023, covering S\u0026atilde;o Paulo, Rio de Janeiro, and Bras\u0026iacute;lia), representing\u0026thinsp;\u0026lt;\u0026thinsp;5% of the study period and \u0026lt;\u0026thinsp;20% of the national population.\u003c/p\u003e\u003cp\u003eThese limited data were aggregated to monthly means and are presented descriptively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to document critical deficiencies in environmental health surveillance infrastructure; however, they were not included in statistical models due to insufficient temporal and spatial coverage for meaningful inference. This data gap reflects broader challenges in Brazil's environmental monitoring capacity and represents a major limitation for integrated environmental health risk assessment in Latin America.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003ePrimary Models\u003c/p\u003e\u003cp\u003eWe used generalized additive models (GAMs) with quasi-Poisson distributions to assess associations between meteorological variables and monthly SAH incidence, accounting for overdispersion commonly observed in count data (37). The base model structure incorporated a penalized cubic regression spline for long-term temporal trends (7 degrees of freedom per year of observation), cyclic cubic splines for seasonal patterns (12 knots), state-specific effects to account for regional baseline differences, and a population offset term (log-transformed)\u003c/p\u003e\u003cp\u003eMeteorological exposures (temperature, humidity, atmospheric pressure, precipitation) were modeled as smooth terms using thin-plate regression splines. To assess potential delayed effects, we implemented distributed lag non-linear models (DLNMs) using cross-basis functions that simultaneously model non-linear exposure-response relationships and lagged effects up to 3 months (37). The temperature-lag cross-basis function utilized natural cubic splines with three internal knots placed at the 10th, 50th, and 90th percentiles of the temperature distribution, and a natural cubic spline for the lag dimension with three equally spaced knots.\u003c/p\u003e\u003cp\u003eAll continuous meteorological variables were standardized (z-scores: [x - mean]/SD) to enable direct comparison of effect sizes across different measurement scales.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSeasonal Stratification\u003c/h3\u003e\n\u003cp\u003eWe examined effect modification by season according to Southern Hemisphere meteorological definitions: summer (December\u0026ndash;February), autumn (March\u0026ndash;May), winter (June\u0026ndash;August), and spring (September\u0026ndash;November). Season-specific linear regression models assessed temperature-SAH associations within each period:\u003c/p\u003e\u003cp\u003eSAH admissions\u0026thinsp;=\u0026thinsp;β₀ + β₁\u0026middot;mean temperature\u0026thinsp;+\u0026thinsp;β₂\u0026middot;temperature range\u0026thinsp;+\u0026thinsp;ε\u003c/p\u003e\u003cp\u003ewhere β₁ represents the change in monthly admissions per 1\u0026deg;C increase in mean temperature, and β₂ captures the effect of diurnal temperature variation (maximum - minimum). Models were adjusted for precipitation and humidity as potential confounders.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analyses\u003c/h2\u003e\u003cp\u003eStratified analyses were performed to identify vulnerable populations and regional variations, age group: \u0026lt;65 years vs. \u0026ge;65 years (assessing thermoregulatory vulnerability in the elderly), sex: male vs. female (exploring potential differences), geographic region: North (equatorial/tropical), Northeast (tropical/semi-arid), Central-West (tropical savanna), Southeast (tropical/subtropical transition), and South (subtropical, coldest winters), relative risks (RR) and 95% confidence intervals (CI) were estimated for each stratum, with interaction terms tested using likelihood ratio tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant),\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSensitivity Analyses\u003c/h3\u003e\n\u003cp\u003eModel robustness was evaluated through, varying degrees of freedom for time trends (5\u0026ndash;10 df/year), Testing alternative lag periods (0\u0026ndash;6 months), Excluding the first and last 6 months of observations to evaluate boundary artifacts, Comparing quasi-Poisson with, negative binomial regression models, Testing models with and without correlated variables (e.g., removing humidity/precipitation when examining temperature), re-running analyses after excluding months with extreme SAH counts (\u0026gt;\u0026thinsp;2 SD from mean)\u003c/p\u003e\n\u003ch3\u003eModel Diagnostics\u003c/h3\u003e\n\u003cp\u003eWe assessed model adequacy through Examination of deviance residuals for temporal patterns and heteroscedasticity, Partial autocorrelation function (PACF) plots to detect residual serial correlation (up to 12 lags), Quantile-quantile (Q-Q) plots comparing observed vs. theoretical residual distributions, Mean absolute percentage error (MAPE) between observed and fitted values, Variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5) to assess multicollinearity among meteorological covariates, and Dispersion parameter φ from quasi-Poisson models (φ\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates overdispersion)\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSoftware and Reproducibility\u003c/h2\u003e\u003cp\u003eAll analyses were performed in Python version 3.11 using statsmodels (v0.14), scipy (v1.11), pandas (v2.0), and pygam (v0.8) libraries. Graphical outputs were generated using matplotlib (version 3.7) and seaborn (version 0.12). Complete analysis code and aggregated data are available from the corresponding author upon reasonable request. Individual-level data cannot be shared due to data protection regulations, but aggregated monthly counts by state, age group, and sex are available.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003eThis study utilized anonymized, publicly available aggregate data that precluded individual identification. The study was exempt from institutional review board approval under Brazilian National Health Council Resolution 510/2016, which exempts research using de-identified public databases. No individual consent was required, given the use of aggregate administrative data.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBetween January 2020 and December 2024, a total of 3,323 hospital admissions for subarachnoid hemorrhage (SAH) were recorded in Brazil's public health system. Mean monthly admissions were 55.4 (SD\u0026thinsp;=\u0026thinsp;12.3, range: 28\u0026ndash;89). The highest seasonal incidence occurred in autumn (26.4%, n\u0026thinsp;=\u0026thinsp;877) and winter (25.4%, n\u0026thinsp;=\u0026thinsp;844), compared to spring (23.9%, n\u0026thinsp;=\u0026thinsp;794) and summer (24.3%, n\u0026thinsp;=\u0026thinsp;808). The mean age at admission was 54.7 years (SD\u0026thinsp;=\u0026thinsp;13.2), with 58.3% of patients being female. Regional distribution showed the highest admission rates in the Southeast (42.1%) and South (28.6%) regions.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eTemporal and Seasonal Patterns\u003c/h2\u003e\u003cp\u003eMonthly time-series analysis revealed consistent peaks in SAH admissions during colder months, particularly from May to July (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Seasonal temperature patterns showed marked differences: the mean winter temperature (22.2\u0026deg;C, SD\u0026thinsp;=\u0026thinsp;2.8\u0026deg;C) was 3.4\u0026deg;C lower than the summer temperature (25.6\u0026deg;C, SD\u0026thinsp;=\u0026thinsp;2.1\u0026deg;C). The temporal correspondence between lower temperatures and higher SAH incidence is visually apparent throughout the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eTemperature-SAH Associations\u003c/h2\u003e\u003cp\u003eSeason-stratified linear regression analyses demonstrated significant negative associations between temperature and SAH admissions in autumn (β = \u0026minus;8.3 admissions per \u0026deg;C; 95% CI: \u0026minus;11.2, \u0026minus;\u0026thinsp;5.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and winter (β = \u0026minus;7.1 per \u0026deg;C; 95% CI: \u0026minus;9.8, \u0026minus;\u0026thinsp;4.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In spring, the association was weaker and marginally significant (β = \u0026minus;3.2 per \u0026deg;C; 95% CI: \u0026minus;6.8, 0.4; p\u0026thinsp;=\u0026thinsp;0.08), while summer showed no meaningful relationship (β = \u0026minus;0.4 per \u0026deg;C; 95% CI: \u0026minus;3.1, 2.3; p\u0026thinsp;=\u0026thinsp;0.64) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStandardized analyses confirmed the inverse temperature-SAH relationship, with correlation coefficients of r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.42 (p\u0026thinsp;=\u0026thinsp;0.02) for maximum temperature and r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.38 (p\u0026thinsp;=\u0026thinsp;0.04) for minimum temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The quasi-Poisson GAM showed adequate fit (dispersion parameter φ\u0026thinsp;=\u0026thinsp;1.32, indicating moderate overdispersion), with no substantial residual autocorrelation (PACF\u0026thinsp;\u0026lt;\u0026thinsp;0.15 for all lags).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eDistributed Lag Analysis\u003c/h2\u003e\u003cp\u003eDLNM analysis revealed that the strongest temperature effect occurred within the same month (lag 0), with a relative risk of 1.18 (95% CI: 1.09\u0026ndash;1.28) per 1\u0026deg;C decrease below the median temperature. Cumulative effects persisted for up to 2 months, though effect sizes diminished at longer lags. No significant non-linear temperature effects were detected beyond the linear relationship.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analyses\u003c/h2\u003e\u003cp\u003eStronger temperature-SAH associations were observed in the South region (β = \u0026minus;12.4 per \u0026deg;C; 95% CI: \u0026minus;17.8, \u0026minus;\u0026thinsp;7.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to other regions (p for interaction\u0026thinsp;=\u0026thinsp;0.03). Age-stratified analysis showed amplified effects among individuals\u0026thinsp;\u0026ge;\u0026thinsp;65 years (RR\u0026thinsp;=\u0026thinsp;1.24 per 1\u0026deg;C decrease; 95% CI: 1.12, 1.37) compared to younger adults (RR\u0026thinsp;=\u0026thinsp;1.11; 95% CI: 1.03, 1.20; p for interaction\u0026thinsp;=\u0026thinsp;0.04). No significant sex differences were detected (p\u0026thinsp;=\u0026thinsp;0.42).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eAir Pollution Data Limitations\u003c/h2\u003e\u003cp\u003eAir quality data were severely limited. Only three months of PM₁₀ measurements from urban stations were available (April\u0026ndash;June 2023), precluding time-series analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These sparse data are presented to document surveillance infrastructure deficiencies rather than to draw epidemiological inferences.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis five-year nationwide ecological analysis reveals a clear inverse relationship between ambient temperature and subarachnoid hemorrhage (SAH) hospital admissions in Brazil, with a peak incidence during the colder autumn and winter months. These findings extend evidence from temperate regions (8,9,11,15) to tropical and subtropical settings, demonstrating that even moderate seasonal temperature variations (a mean difference of 3.4\u0026deg;C between winter and summer) significantly influence SAH risk. The results support the hypothesis that environmental factors modulate cerebrovascular vulnerability beyond established individual risk factors (4,19).\u003c/p\u003e\u003cp\u003eThe observed temperature-SAH association is biologically plausible through well-established physiological pathways. Cold exposure triggers sympathetic nervous system activation, leading to peripheral vasoconstriction and increased systemic vascular resistance, which in turn elevates arterial blood pressure and may destabilize aneurysmal walls (7,38). Cold temperatures also promote prothrombotic changes, including elevated fibrinogen levels, increased platelet aggregation, and higher blood viscosity (10), creating conditions conducive to aneurysm rupture. These mechanisms, extensively documented in European and Asian populations (11,15), appear to be consistent with the patterns observed in our Brazilian cohort.\u003c/p\u003e\u003cp\u003eThe stronger associations in southern Brazil, where winters are more pronounced, and among elderly individuals (\u0026ge;\u0026thinsp;65 years), who have diminished thermoregulatory capacity, support the cold-exposure hypothesis. The immediate effect (lag 0) and short persistence (up to 2 months) align with acute hemodynamic responses rather than cumulative exposures.\u003c/p\u003e\u003cp\u003eThe near-complete absence of air pollution data represents a critical limitation\u0026mdash;not primarily of this study, but of Brazil's environmental health surveillance infrastructure. Despite evidence from other countries linking particulate matter exposure to cerebrovascular events through systemic inflammation, endothelial dysfunction, and autonomic imbalance (13,18\u0026ndash;20), we were unable to evaluate this pathway. Only three months of PM₁₀ data were retrievable, exemplifying broader challenges in environmental health monitoring across Latin America, where integrated air quality surveillance remains fragmented and geographically limited (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis surveillance gap has substantial implications beyond research limitations. The inability to assess synergistic effects between temperature and air pollution on SAH risk represents a missed opportunity for comprehensive environmental health risk assessment. Future studies integrating long-term, high-resolution air quality data with health outcomes are essential, but first require investment in monitoring infrastructure.\u003c/p\u003e\u003cp\u003eThese findings have important implications for neurocritical care planning in Brazil. The consistent seasonal pattern suggests opportunities for targeted capacity planning, with neurocritical care resources accounting for predictable autumn/winter surges in SAH admissions, particularly in southern states where temperature effects are most pronounced (39). Integrating meteorological data\u0026mdash;including temperature drops, atmospheric pressure changes, and humidity shifts\u0026mdash;into public health surveillance could enable proactive capacity adjustments (40,41). Such environmental early warning systems could alert healthcare facilities to periods of elevated SAH risk, facilitating the preparation of staffing and resources before seasonal peaks (41,42).\u003c/p\u003e\u003cp\u003eClimate change adds further complexity to this scenario. Increasing temperature variability and extreme weather events may amplify seasonal disease burdens (14,24,40). While long-term warming might reduce winter-associated peaks in some areas, increased climate volatility and abrupt temperature transitions could trigger acute hemodynamic stresses in vulnerable populations (43,44). Given Brazil's regional heterogeneity, climate change impacts on SAH incidence are likely to be uneven, potentially exacerbating existing health inequalities between northern tropical and southern subtropical regions (40).\u003c/p\u003e\u003cp\u003eThis study has inherent limitations. The ecological design precludes individual-level causal inference and cannot determine whether specific patients were exposed to cold immediately before rupture. Data were restricted to public system admissions (~\u0026thinsp;75% of the population), potentially missing privately insured patients with different risk profiles. We lacked individual-level confounders (hypertension control, aneurysm characteristics, medications). Monthly aggregation may obscure the acute effects of temperature fluctuations. Air pollution analysis was impossible due to infrastructure deficiencies. The five-year period, while capturing robust seasonal patterns, may not reflect long-term climate trends. Despite these constraints, this nationwide analysis provides novel evidence that moderate climatic fluctuations meaningfully influence SAH occurrence in a climatically diverse middle-income country, with implications for environmental health surveillance and neurocritical care preparedness across Latin America.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSAH admissions in Brazil increase during colder months, with a consistent inverse association between temperature and incidence. This pattern is biologically plausible, reflecting cold-induced sympathetic activation, peripheral vasoconstriction, and prothrombotic changes. These findings extend evidence from temperate regions to tropical and subtropical settings, demonstrating that even moderate seasonal temperature variations can meaningfully influence SAH risk in climatically diverse populations.\u003c/p\u003e\n\u003cp\u003eThe critical gap in air pollution monitoring infrastructure represents a significant impediment to comprehensive environmental health risk assessment in Brazil, highlighting the urgent need for investment in environmental surveillance across Latin America. Findings suggest the need to incorporate seasonal considerations into neurocritical care planning and integrate meteorological indicators into health surveillance systems. As climate variability intensifies, adaptive surveillance strategies and seasonal preparedness will be crucial in preventing disproportionate impacts on vulnerable regions and populations. Future research should prioritize establishing comprehensive air quality monitoring networks and examining individual-level interactions between environmental exposures and cerebrovascular risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompliance with Instructions to Authors: The authors confirm that this manuscript complies with all instructions to authors provided by the \u003cem\u003eNeurocritical Care\u003c/em\u003e journal, including formatting, word count, figure/table specifications, reference style, and ethical requirements.\u003c/p\u003e\n\u003cp\u003eGeneral Statement: All authors have read and approved the submitted manuscript. This manuscript has not been submitted elsewhere nor published elsewhere in whole or in part. All authors confirm the integrity of the work and take responsibility for the content and conclusions presented herein.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; Contributions: All authors meet the ICMJE criteria for authorship and have approved the final manuscript. Individual contributions were as follows: Daniela Laranja Gomes Rodrigues (DLGR): Study conceptualization, design, data collection coordination, data analysis, manuscript drafting, and project supervision. Jo\u0026atilde;o Brainer Clares de Andrade (JBCA): Study design, critical review of manuscript, methodological supervision, and approval of final version. Gisele Sampaio Silva (GSS): Study design, critical revision for important intellectual content, and approval of final version for submission.\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the interpretation of data and critical revision of the manuscript. All authors approved the final version and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eFunding: This work has no funding.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest: The authors declare no competing interests, financial or otherwise, related to the study design, execution, analysis, interpretation, writing, or decision to publish this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics Approval and Informed Consent: \u0026nbsp;The study utilized only aggregated, de-identified service data without individual patient identifiers. Individual informed consent was not required due to the use of non-identifiable aggregated data. No patient-level data were collected or analyzed.\u003c/p\u003e\n\u003cp\u003eUse of Generative AI and AI-Assisted Technologies: During the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-4) to assist with English language editing, grammar review, and translation of Portuguese content into English. All AI-generated content was thoroughly reviewed, edited, and verified by the authors to ensure accuracy, clarity, and scientific rigor. The authors take full responsibility for the content and integrity of this publication. AI tools were not used for data analysis, interpretation, or the generation of scientific conclusions.\u003c/p\u003e\n\u003cp\u003eData Availability: Hospitalization data are publicly available from DATASUS (http://datasus.saude.gov.br/). Meteorological data are available from INMET (https://portal.inmet.gov.br/). Limited air pollution data were obtained from OpenAQ (https://openaq.org/). Aggregated analysis datasets and code are available from the corresponding author upon reasonable request, subject to data protection regulations.\u003c/p\u003e\n\u003cp\u003eGuarantor: Daniela Laranja Gomes Rodrigues serves as the guarantor for this manuscript and accepts full responsibility for the work, including the study design, access to data, and the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJustification:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eStudy conducted without external funding through institutional social responsibility initiatives\u003c/li\u003e\n \u003cli\u003eNo access to publication budgets or research grants\u003c/li\u003e\n \u003cli\u003eAuthors from a middle-income country (Brazil), where APC represents a substantial barrier\u003c/li\u003e\n \u003cli\u003eFindings address critical knowledge gap with direct implications for neurocritical care planning globally\u003c/li\u003e\n \u003cli\u003eCommitment to open data/code sharing to maximize impact\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe believe this research contributes important evidence from underrepresented populations to the neurocritical care literature. A waiver would enable maximum dissemination to clinicians and policymakers in resource-limited settings who would benefit most.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors meet the ICMJE criteria for authorship and have approved the final manuscript. Individual contributions were as follows: Daniela Laranja Gomes Rodrigues (DLGR): Study conceptualization, design, data collection coordination, data analysis, manuscript drafting, and project supervision. Jo\u0026atilde;o Brainer Clares de Andrade (JBCA): Study design, critical review of manuscript, methodological supervision, and approval of final version. Gisele Sampaio Silva (GSS): Study design, critical revision for important intellectual content, and approval of final version for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eMacdonald RL, Schweizer TA. Spontaneous subarachnoid haemorrhage. The Lancet. 2017 Feb;389(10069):655\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGBD 2021 Global Subarachnoid Hemorrhage Risk Factors Collaborators, Rautalin I, Volovici V, Stark BA, Johnson CO, Kaprio J, et al. Global, Regional, and National Burden of Nontraumatic Subarachnoid Hemorrhage: The Global Burden of Disease Study 2021. JAMA Neurol. 2025 Aug 1;82(8):765.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRinkel GJ, Algra A. Long-term outcomes of patients with aneurysmal subarachnoid haemorrhage. Lancet Neurol. 2011 Apr;10(4):349\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJimenez-Conde J, Ois A, Gomis M, Rodriguez-Campello A, Cuadrado-Godia E, Subirana I, et al. Weather as a Trigger of Stroke. Cerebrovasc Dis. 2008;26(4):348\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChen SJ, Lee M, Wu BC, Muo CH, Sung FC, Chen PC. 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Eur J Neurol. 2002 Sept;9(5):511\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFischer T, Johnsen SP, Pedersen L, Gaist D, S\u0026oslash;rensen HT, Rothman KJ. Seasonal variation in hospitalization and case fatality of subarachnoid hemorrhage - a nationwide danish study on 9,367 patients. Neuroepidemiology. 2005;24(1\u0026ndash;2):32\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKeatinge WR, Coleshaw SR, Cotter F, Mattock M, Murphy M, Chelliah R. Increases in platelet and red cell counts, blood viscosity, and arterial pressure during mild surface cooling: factors in mortality from coronary and cerebral thrombosis in winter. BMJ. 1984 Nov 24;289(6456):1405\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eInagawa T. Seasonal variation in the incidence of aneurysmal subarachnoid hemorrhage in hospital- and community-based studies. 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Radiology. 1998 Dec;209(3):667\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eModesti PA, Morabito M, Bertolozzi I, Massetti L, Panci G, Lumachi C, et al. Weather-Related Changes in 24-Hour Blood Pressure Profile: Effects of Age and Implications for Hypertension Management. Hypertension. 2006 Feb;47(2):155\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWen J, Zou L, Jiang Z, Li Y, Tao J, Liu Y, et al. Association between ambient temperature and risk of stroke morbidity and mortality: A systematic review and meta-analysis. Brain Behav. 2023 July;13(7):e3078.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMomperousse D, Delnevo CD, Lewis MJ. Exploring the seasonality of cigarette-smoking behaviour: Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Tob Control. 2007 Feb;16(1):69.2\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChandra S, Chaloupka FJ. Seasonality in cigarette sales: patterns and implications for tobacco control. Tob Control. 2003 Mar;12(1):105\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDa Silva VO, Manicardi RP, Da Silva BM, Pereira CU, Rabelo NN. Influence of Weather Conditions on the Incidence of SAH. Arq Bras Neurocir Braz Neurosurg. 2025 June;44(02):e111\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDa Silva Paiva L, Schoueri JHM, De Alcantara Sousa LV, Raimundo RD, Da Silva Maciel E, Correa JA, et al. Regional differences in the temporal evolution of stroke: a population-based study of Brazil according to sex in individuals aged 15\u0026ndash;49 years between 1997 and 2012. BMC Res Notes. 2018 Dec;11(1):326.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDe Moraes Bernal H, De Abreu LC, Pinheiro Bezerra IM, Adami F, Takasu JM, Ji Young Suh JV, et al. Incidence of hospitalization and mortality due to stroke in young adults, residents of developed regions in Brazil, 2008\u0026ndash;2018. Gall S, editor. PLOS ONE. 2020 Nov 16;15(11):e0242248.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLotufo PA. Stroke in Brazil: a neglected disease. Sao Paulo Med J. 2005 Jan;123(1):3\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAvezum \u0026Aacute;, Costa-Filho FF, Pieri A, Martins SO, Marin-Neto JA. Stroke in Latin America: Burden of Disease and Opportunities for Prevention. Glob Heart. 2015 Dec 1;10(4):323.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRodrigues DLG. Challenges and opportunities in the Brazilian Unified Health System: Pathways to sustainability and equity. Public Health. 2025 Oct;247:105844.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. 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The cutaneous vasoconstrictor response in lower extremities during whole-body and local skin cooling in young women with a cold constitution. J Physiol Sci. 2015 Sept;65(5):397\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSilva GS, Maldonado NJ, Mejia-Mantilla JH, Ortega-Gutierrez S, Claassen J, Varelas P, et al. Neuroemergencies in South America: How to Fill in the Gaps? Neurocrit Care. 2019;31(3):573\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eP\u0026eacute;res WE, Ribeiro AFS, Russo A, Nunes B. The Association between Air Temperature and Mortality in Two Brazilian Health Regions. Climate. 2020 Jan 19;8(1):16.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWorld Health Organization. Operational framework for building climate resilient health systems [Internet]. Geneva: World Health Organization; 2015 [cited 2025 Aug 18]. 47 p. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/189951\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYarova K, Solodovnikova Y, Son A. The Role of Meteorological Factors in Cerebral Anterior versus Posterior Circulation Aneurysm Rupture. World Neurosurg. 2025 Sept;201:124269.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet. 2015 July;386(9991):369\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZanobetti A, O\u0026rsquo;Neill MS, Gronlund CJ, Schwartz JD. Summer temperature variability and long-term survival among elderly people with chronic disease. Proc Natl Acad Sci. 2012 Apr 24;109(17):6608\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Limited PM\u003c/strong\u003e\u003cstrong\u003e₁₀\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;data available in Brazil (2023)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.4627%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1958%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003c/strong\u003e\u003cstrong\u003e₁₀\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u0026micro;g/m\u0026sup3;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAH Admissions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.4627%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApril 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1958%;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3414%;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.4627%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMay 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1958%;\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3414%;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.4627%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJune 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1958%;\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3414%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eLegend.\u003c/em\u003e Limited PM₁₀ data available for Brazil (April-June 2023 only). Data from selected urban monitoring stations (S\u0026atilde;o Paulo, Rio de Janeiro, Bras\u0026iacute;lia) represent \u0026lt;5% of the study period and \u0026lt;20% of the national population. PM₁₀, particulate matter \u0026le;10 micrometers; SAH, subarachnoid hemorrhage.\u003c/p\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Subarachnoid Hemorrhage, Climate Change, Air Pollution, Health Inequalities, Weather, Latin America","lastPublishedDoi":"10.21203/rs.3.rs-7792143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7792143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSubarachnoid hemorrhage (SAH) carries high mortality worldwide. Seasonal patterns have been documented in temperate regions, but evidence from tropical and subtropical populations remains limited.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eWe investigated associations between meteorological variables and SAH hospital admissions in Brazil across diverse climate zones.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted an ecological time-series analysis linking nationwide SAH hospitalizations (ICD-10: I60.0\u0026ndash;I60.9) from Brazil's Unified Health System with meteorological data from the National Institute of Meteorology, 2020\u0026ndash;2024. Generalized additive models with distributed lag structures were used to assess the relationships between temperature, humidity, atmospheric pressure, and monthly SAH incidence. Air pollution analysis was attempted but revealed critical surveillance gaps. Stratified analyses explored effect modification by season, age, and region.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 3,323 SAH admissions, incidence peaked during autumn (26.4%) and winter (25.4%) when mean temperatures were 3.4\u0026deg;C lower than summer. Temperature showed inverse associations with admissions in autumn (β = \u0026minus;8.3 per \u0026deg;C; 95% CI: \u0026minus;11.2, \u0026minus;\u0026thinsp;5.4) and winter (β = \u0026minus;7.1 per \u0026deg;C; 95% CI: \u0026minus;9.8, \u0026minus;\u0026thinsp;4.4). Effects were stronger in southern regions and among individuals aged 65 years or older. Air quality data were available for only three months, which precluded analysis and highlighted infrastructure deficiencies.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e\u003cp\u003eSAH admissions in Brazil increase during colder months with a dose-response relationship. Findings align with cold-induced sympathetic activation and blood pressure elevation. The absence of integrated air pollution monitoring represents a major surveillance gap. Results support the incorporation of meteorological indicators into neurocritical care planning and underscore the urgent need for an environmental health data infrastructure in Latin America.\u003c/p\u003e","manuscriptTitle":"When the Weather Turns Cold: Seasonal Patterns of Subarachnoid Hemorrhage in a Tropical Country","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 15:47:30","doi":"10.21203/rs.3.rs-7792143/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-22T17:55:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T14:08:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Neurocritical Care","date":"2025-10-16T17:57:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T21:00:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neurocritical Care","date":"2025-10-10T14:41:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"654a3796-c6ba-459d-96f7-97b6023b1a09","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T19:57:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 15:47:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7792143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7792143","identity":"rs-7792143","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}