Impact of Cold Spells on Community-Acquired Pneumonia Incidence

Impact of Cold Spells on Community-Acquired Pneumonia Incidence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Cold Spells on Community-Acquired Pneumonia Incidence Kadir Burak Akgün, Demet Polat Yuluğ, Mehmet Karadağ, Merve Sinem Oğuz, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7515402/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Climate change, characterized by extreme temperature fluctuations, has emerged as a significant risk factor for respiratory diseases, including pneumonia. Most previous studies have focused on hospital-acquired pneumonia or ICD-based data, which may have led to misclassification. This study aimed to evaluate the impact of extreme temperature events, particularly cold spells, on the incidence of community-acquired pneumonia in XXX, a coastal city in the Mediterranean region. Methods Meteorological data were obtained from the XXX Meteorology Station, and pneumonia cases diagnosed by pulmonologists were collected from six hospitals between January 2 and December 30, 2024. Generalized Additive Poisson Regression Models were applied to assess the lag effects (lag 0–3 days) of maximum and minimum temperatures on pneumonia incidence, adjusting for long-term trends and seasonality. Results A total of 13,651 pneumonia cases were recorded, of whom 51.5% (n = 7034) were male. Minimum temperature at lag 1 day was significantly associated with an increased risk of pneumonia (RR: 0.984, 95% CI: 0.973–0.996, p 65 years). Conclusions Exposure to cold temperatures was associated with an increased incidence of pneumonia, particularly one day after exposure. These findings highlight the need for targeted public health interventions during cold spells, especially for vulnerable populations. Considering the longer incubation periods of pathogenic microbiomes, this association may be linked to the increased virulence of colonizing microorganisms triggered by cold weather conditions. community-acquired pneumonia cold spells climate change and health Introduction Although pneumonia is classically defined as the microbial invasion of the lung parenchyma, its pathophysiology also involves host susceptibility, dysregulated inflammatory responses, and alterations in the microbial population ( 1 ). Climate change has emerged as a critical focus in global health research due to its significant environmental and societal impacts, and its effects are particularly anticipated on the lungs, which are directly connected to the ecosystem. ( 2 , 3 ). Recent studies have increasingly highlighted the adverse effects of climate variability, particularly heatwaves and cold spells, on respiratory health. These temperature extremes can exacerbate respiratory infections and contribute to increased pneumonia incidence and severity(-4-7). Previous studies investigating the link between climate change and pneumonia have generally been conducted on a local or regional scale, emphasizing the influence of specific climatic and environmental conditions( 8 – 10 ). Given the geographical and climatic variability, regional analyses are particularly relevant for understanding the climate-health nexus( 11 , 12 ). This study aims to evaluate the impact of extreme temperature events, including both heatwaves and cold spells, on the incidence of pneumonia in XXX, a coastal city in the Eastern Mediterranean region of Turkey. Given XXX’s geographical location and climate characteristics, it serves as a critical setting to assess the health impacts of extreme temperature fluctuations in a Mediterranean coastal region. While previous epidemiological studies primarily rely on ICD codes to identify pneumonia cases, we adopted a clinically focused approach. However, ICD-based studies may inadvertently include hospital-acquired pneumonia or conditions mimicking pneumonia, leading to potential misclassification. Prior studies have predominantly focused on the prevalence and mortality of pneumonia using ICD codes, often encompassing both community-acquired and hospital-acquired infections. In contrast, our study specifically targeted outpatient cases, aiming to obtain a more accurate assessment of community-acquired pneumonia while minimizing the influence of hospital-acquired infections characterized by distinct microbial patterns and clinical management. Initially, we used ICD codes (J12-J18 and their subcategories) to screen potential pneumonia cases. However, to ensure diagnostic accuracy and minimize misclassification, only cases diagnosed by pulmonologists were included in the analysis. This method provides more reliable data for assessing the health impacts of climate variability. Methods Meteorological data for this research was provided by the "XXX Meteorology Station" of the Turkish State Meteorological Service, with meteorological data collected from 29th December 2023 to 30th December 2024. The key feature of this station is that it is located within a 50 km radius of the hospitals from which the data was collected. A total of six hospitals in the region participated in the study. Initially, data for patients aged 18 years and older with a diagnosis of pneumonia were screened based on ICD codes J12-J18 and their subcategories. The ICD codes were provided to chest disease specialists, who reviewed their outpatient records and identified cases they had definitively diagnosed as pneumonia between January 2, 2024, and December 30, 2024. Only these clinically confirmed cases were included in the study to ensure diagnostic accuracy. Patients’ age and gender information were recorded. Cases of hospital-acquired pneumonia were excluded, as hospital ventilation and colonization may not reflect the impact of external climatic factors. Additionally, cases diagnosed by medical doctors other than pulmonologists were also excluded. Statictical analysis The normality of the data distribution was tested using the Shapiro-Wilk test. The Student’s t-test and Mann-Whitney U test were used to compare two independent groups of variables, depending on whether they followed a normal distribution or not, respectively. Generalized Additive Poisson Regression Models (Aldrin & Hobæk Haff, 2005; Ravindra et al., 2019) were used to analyze the daily and lag effects (lag 0 = same day; lag 1 = first day; lag 2 = second day; lag 3 = third day) of maximum and minimum temperature levels on the number of pneumonia cases. A log link function was used for the smoothing function, and penalized smoothing splines were applied to adjust for seasonal patterns and long-term trends in disease morbidity, with time included as a smoothing variable. All univariate statistical analyses were performed using SPSS for Windows (version 24.0), and the generalized additive Poisson regression models were implemented using the mgcv package (version 3.4.1) in R for generalized additive modeling (GAM). The gam function in mgcv was used to estimate the smoothing parameters via the Generalized Cross-Validation (GCV) criterion. The optimal degrees of freedom were automatically selected using GCV based on the Unbiased Risk Estimator (UBRE) criterion. Relative Risk (RR) and 95% Confidence Intervals (CI) were calculated to indicate the direction and magnitude of the effects. Results Between January 2 and December 30, 2024, a total of 13,651 pneumonia-related outpatient visits were recorded over 245 weekdays, with 51.5% (n = 7034) of the patients being male. The descriptive statistics (median [IQR]) for air pollutants and meteorological variables during the study period are presented in Table 1 . Table 1 Descriptive statistics Parameters N / median [IQR] Admission polyclinic Total = 13651 (male = 7034;female = 6617;≤65 age = 9024;>65 = 4627) Max temp ( o C) 25,4 (17,6–31,5) Min tempreture( o C) 13,1 ( 8 , 9 – 22 , 3 ) Average tempreture ( o C) 19,4 (12–27,1) IQR(inter-quartile range): [Q1-Q3] As shown in Table 2 , while the number of pneumonia cases was associated with the minimum temperature at a lag of 2 days, the minimum temperature at a lag of 1 day (RR: 0.984, 95% CI = 0.973–0.996) was found to be significantly associated with an increase in pneumonia cases (p < 0.05). A decrease in temperatures at lag 1 led to an increase in pneumonia cases. On the other hand, the effects of meteorological factors in male and female groups are presented in Table 2 , while their effects across age groups are shown in Table 3 . Table 2 Results of generalized additive Poisson models for predicting the number of admission …. disease polyclinic (stratified by sex (gender)) Variables Male RR (CI 95%) Female RR (CI 95%) Total RR (CI 95%) Max Temp - lag0 1.005 (0.991–1.019) 1.002 (0.978–1.016) 1.003 (0.993–1.014) Max Temp – lag1 1.006 (0.991–1.021) 1.008 (0.993–1.024) 1.007 (0.996–1.018) Max Temp – lag2 1.010 (0.995–1.026) 1.005 (0.990–1.021) 1.008 (0.997–1.019) Max Temp – lag3 0.994 (0.981–1.007) 0.996 (0.983–1.009) 0.995 (0.986–1.004) Min Temp – lag0 0.999 (0.985–1.014) 1.007 (0.992–1.022) 1.003 (0.992–1.013) Min Temp – lag1 0.973 (0.957–0.989) 0.995 (0.979–1.011) 0.984 (0.973–0.996) Min Temp – lag2 1.016 (1.001–1.031) 1.005 (0.991–1.020) 1.011 (1.001–1.022) Min Temp – lag3 0.998 (0.985–1.011) 1.001 (0.987–1.014) 0.999 (0.990–1.009) RR, Relative Risk, bold p < 0.05 Table 3 Results of generalized additive Poisson models for predicting the number of admission …. disease polyclinic (stratified by age) Variables Age 65 RR (CI 95%) Max Temp - lag0 1,003 (0,990-1,015) 1,005 (0,988-1,023) Max Temp – lag1 1,005 (0,992-1,019) 1,010 (0,992-1,029) Max Temp – lag2 1,001 (0,987-1,014) 1,023 (1,004 − 1,041) Max Temp – lag3 1,000 (0,989-1,012) 0,985 (0,969-1,001) Min Temp – lag0 1,009 (0,996-1,022) 0,992 (0,974-1,010) Min Temp – lag1 0,981 (0,967-0,995) 0,989 (0,970-1,008) Min Temp – lag2 1,004 (0,991-1,017) 1,024 (1,006 − 1,042) Min Temp – lag3 1,004 (0,993-1,016) 0,990 (0,974-1,006) • RR, Relative Risk, bold p < 0.05 Discussion XXX, a coastal city in the Eastern Mediterranean with a typical Mediterranean climate and notable socioeconomic diversity, can serve as a representative model for other similar regions experiencing climate change. Our study demonstrates a statistically significant association between cold temperatures and increased pneumonia incidence, with the most pronounced effect observed one day after cold exposure. This finding aligns with previous research indicating that abrupt temperature drops can increase susceptibility to respiratory infections by compromising mucosal defenses and enhancing viral replication( 13 ). Additionally, our results are consistent with studies from similar climatic regions, suggesting that cold spells may act as a trigger for pneumonia exacerbations, particularly among vulnerable populations such as the elderly and those with pre-existing lung diseases( 14 – 16 ). Moreover, our analysis reveals that while the effect of cold temperatures was statistically significant on the next day (lag 1), a similar but less pronounced effect was also noted on the day after next day (lag 2), suggesting a potential impact of cold exposure on pneumonia incidence. This delayed effect could be attributed to a time-dependent inflammatory response or a gradual change in ambient temperature, further emphasizing the need for timely public health interventions during cold spells. Such findings are supported by several studies highlighting the delayed effects of extreme weather events on respiratory infection rates( 17 , 18 ). Similar to residential buildings, institutions such as hospitals have their own internal ventilation systems designed to mitigate the impact of extreme cold and heat( 19 , 20 ). Additionally, due to frequent disinfection procedures and the more common use of antibiotics, the microbiota within hospitals differs significantly from that of the external environment( 21 ). Hospital-acquired pneumonias (HAPs) are a leading cause of nosocomial infections and represent the most common cause of nosocomial infection-related mortality( 22 , 23 ). In our study, we chose to focus on community-acquired pneumonia (CAP) in outpatients, as hospital-acquired pneumonias tend to be more localized issues, whereas climate change and related temperature fluctuations are more likely to impact the incidence of CAP. Focusing on CAP cases diagnosed exclusively by pulmonologists enhanced the diagnostic accuracy and reduced misclassification bias, which is a common limitation in studies relying solely on administrative data. This specificity strengthens the internal validity of our findings but may limit comparability with studies including hospitalized cases. Limitations Our study has certain limitations that warrant consideration. Our study utilized data from 2024, considering the impact of the COVID-19 pandemic in 2020–2021 and the disruptions caused by the February 6, 2023 earthquake in the region. The long curfews imposed during the pandemic changed the epidemiological and even environmental data during this period. The significant disruptions in health and public services after the earthquake made it difficult to obtain reliable health and meteorological data from these periods. Consequently, we focused on short-term temperature effects, using daily data with a lag period of up to three days to capture the commonly reported 2- to 3-day incubation period for pneumonia( 24 ). However, this approach may not fully reflect the cumulative or extended impact of prolonged cold spells on pneumonia incidence. Previous studies have suggested that sustained exposure to low temperatures or recurrent cold events can exacerbate respiratory vulnerability, particularly in high-risk populations such as older adults and children( 25 ). Second, while our study focused primarily on the impact of temperature on pneumonia incidence, other environmental factors such as humidity, air pollution, and wind speed were not included in the analysis for the reasons stated above. These variables can independently or synergistically affect respiratory infection risks and may modify the temperature-pneumonia relationship( 26 ). In order to generalize the results obtained by data collection with our method, more studies with longer periods and more parameters are needed. Conclusion Our findings indicate a significant association between lower temperatures and increased pneumonia incidence, particularly evident one and two days following cold weather exposure. This underscores the importance of implementing targeted public health interventions, such as early warning systems and community outreach programs, to mitigate the impact of cold spells on respiratory health, especially in vulnerable populations such as the elderly and those with pre-existing lung conditions. Furthermore, our study highlights the necessity for region-specific analyses in understanding the localized health effects of climate change, as climatic and socio-demographic differences may substantially influence the observed associations. Despite our findings, it is important to acknowledge discrepancies with previous literature, which may be attributable to methodological differences, including varying definitions of exposure, outcome measurement, and analytical approaches. Additionally, while advancements in health information systems have improved data accessibility and integration, epidemiological studies should maintain a strong connection with field data to ensure accurate case ascertainment and context-specific insights. With the increasing use of artificial intelligence in epidemiology, researchers may rely more on desk-based data analysis, potentially moving away from direct field data collection. Future studies should aim to maintain a balance between advanced data analysis and on-the-ground insights to provide a more complete picture of public health impacts. Declarations Ethics approval This study was approved by the Ethics Committee of Hatay Mustafa Kemal University (Approval No: 63, dated January 8, 2025). Given the retrospective and anonymized nature of outpatient data, individual informed consent was waived. The study was conducted in accordance with the ethical standards of the institutional research committee and the principles of the Declaration of Helsinki. Consent for publication Not applicable. This study does not contain any individual person’s data in any form Aviılability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing interests The authors declare no conflicts of interest. Funding No funding was received for this study. Authors’ contributions Conceptualization was performed by KBA, DPY, MK. Data collection provided by KBA, MSO, HYÖ, SN, AK, BNE. Data analysis and interpretation was performed by MK. Manuscript was prepared by KBA, DPY. Project administration by KBA. All authors reviewed the manuscript. Clinical Trial Number Not applicable Acknowledgments We would like to thank the Turkish Meteorological Service for their unconditional support. References Jones BE, Ramirez JA, Oren E, Soni NJ, Sullivan LR, Restrepo MI, et al. Diagnosis and Management of Community-acquired Pneumonia. An Official American Thoracic Society Clinical Practice Guideline. Am J Respir Crit Care Med. 2025 Jul 18. doi: 10.1164/rccm.202507-1692ST. Pirkle LT, Jennings N, Vercammen A, Lawrance EL. Current understanding of the impact of climate change on mental health within UK parliament. Front Public Health. 2022 Sep 16;10:913857. Martins FP, Paschoalotto MAC, Closs J, Bukowski M, Veras MM. 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12:27:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":316581,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7515402/v1/dc5e8480-b511-48cf-b693-8a66d87a6048.pdf"},{"id":93962599,"identity":"ee71082d-7b53-48c4-a272-d631cdf92bf9","added_by":"auto","created_at":"2025-10-20 17:23:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25098,"visible":true,"origin":"","legend":"","description":"","filename":"RAWDATA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7515402/v1/fae6217d1014812b34719e16.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Cold Spells on Community-Acquired Pneumonia Incidence","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAlthough pneumonia is classically defined as the microbial invasion of the lung parenchyma, its pathophysiology also involves host susceptibility, dysregulated inflammatory responses, and alterations in the microbial population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Climate change has emerged as a critical focus in global health research due to its significant environmental and societal impacts, and its effects are particularly anticipated on the lungs, which are directly connected to the ecosystem. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Recent studies have increasingly highlighted the adverse effects of climate variability, particularly heatwaves and cold spells, on respiratory health. These temperature extremes can exacerbate respiratory infections and contribute to increased pneumonia incidence and severity(-4-7).\u003c/p\u003e\u003cp\u003ePrevious studies investigating the link between climate change and pneumonia have generally been conducted on a local or regional scale, emphasizing the influence of specific climatic and environmental conditions(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Given the geographical and climatic variability, regional analyses are particularly relevant for understanding the climate-health nexus(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study aims to evaluate the impact of extreme temperature events, including both heatwaves and cold spells, on the incidence of pneumonia in XXX, a coastal city in the Eastern Mediterranean region of Turkey. Given XXX\u0026rsquo;s geographical location and climate characteristics, it serves as a critical setting to assess the health impacts of extreme temperature fluctuations in a Mediterranean coastal region.\u003c/p\u003e\u003cp\u003eWhile previous epidemiological studies primarily rely on ICD codes to identify pneumonia cases, we adopted a clinically focused approach. However, ICD-based studies may inadvertently include hospital-acquired pneumonia or conditions mimicking pneumonia, leading to potential misclassification. Prior studies have predominantly focused on the prevalence and mortality of pneumonia using ICD codes, often encompassing both community-acquired and hospital-acquired infections.\u003c/p\u003e\u003cp\u003eIn contrast, our study specifically targeted outpatient cases, aiming to obtain a more accurate assessment of community-acquired pneumonia while minimizing the influence of hospital-acquired infections characterized by distinct microbial patterns and clinical management. Initially, we used ICD codes (J12-J18 and their subcategories) to screen potential pneumonia cases. However, to ensure diagnostic accuracy and minimize misclassification, only cases diagnosed by pulmonologists were included in the analysis. This method provides more reliable data for assessing the health impacts of climate variability.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMeteorological data for this research was provided by the \"XXX Meteorology Station\" of the Turkish State Meteorological Service, with meteorological data collected from 29th December 2023 to 30th December 2024. The key feature of this station is that it is located within a 50 km radius of the hospitals from which the data was collected. A total of six hospitals in the region participated in the study.\u003c/p\u003e\u003cp\u003eInitially, data for patients aged 18 years and older with a diagnosis of pneumonia were screened based on ICD codes J12-J18 and their subcategories. The ICD codes were provided to chest disease specialists, who reviewed their outpatient records and identified cases they had definitively diagnosed as pneumonia between January 2, 2024, and December 30, 2024. Only these clinically confirmed cases were included in the study to ensure diagnostic accuracy. Patients\u0026rsquo; age and gender information were recorded.\u003c/p\u003e\u003cp\u003eCases of hospital-acquired pneumonia were excluded, as hospital ventilation and colonization may not reflect the impact of external climatic factors. Additionally, cases diagnosed by medical doctors other than pulmonologists were also excluded.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatictical analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe normality of the data distribution was tested using the Shapiro-Wilk test. The Student\u0026rsquo;s t-test and Mann-Whitney U test were used to compare two independent groups of variables, depending on whether they followed a normal distribution or not, respectively. Generalized Additive Poisson Regression Models (Aldrin \u0026amp; Hob\u0026aelig;k Haff, 2005; Ravindra et al., 2019) were used to analyze the daily and lag effects (lag 0\u0026thinsp;=\u0026thinsp;same day; lag 1\u0026thinsp;=\u0026thinsp;first day; lag 2\u0026thinsp;=\u0026thinsp;second day; lag 3\u0026thinsp;=\u0026thinsp;third day) of maximum and minimum temperature levels on the number of pneumonia cases. A log link function was used for the smoothing function, and penalized smoothing splines were applied to adjust for seasonal patterns and long-term trends in disease morbidity, with time included as a smoothing variable. All univariate statistical analyses were performed using SPSS for Windows (version 24.0), and the generalized additive Poisson regression models were implemented using the \u003cem\u003emgcv\u003c/em\u003e package (version 3.4.1) in R for generalized additive modeling (GAM). The gam function in \u003cem\u003emgcv\u003c/em\u003e was used to estimate the smoothing parameters via the Generalized Cross-Validation (GCV) criterion. The optimal degrees of freedom were automatically selected using GCV based on the Unbiased Risk Estimator (UBRE) criterion. Relative Risk (RR) and 95% Confidence Intervals (CI) were calculated to indicate the direction and magnitude of the effects.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBetween January 2 and December 30, 2024, a total of 13,651 pneumonia-related outpatient visits were recorded over 245 weekdays, with 51.5% (n\u0026thinsp;=\u0026thinsp;7034) of the patients being male. The descriptive statistics (median [IQR]) for air pollutants and meteorological variables during the study period are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN / \u003cem\u003emedian [IQR]\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission polyclinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u0026thinsp;=\u0026thinsp;13651 (male\u0026thinsp;=\u0026thinsp;7034;female\u0026thinsp;=\u0026thinsp;6617;\u0026le;65 age\u0026thinsp;=\u0026thinsp;9024;\u0026gt;65\u0026thinsp;=\u0026thinsp;4627)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax temp (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,4 (17,6\u0026ndash;31,5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin tempreture(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,1 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage tempreture (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19,4 (12\u0026ndash;27,1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eIQR(inter-quartile range): [Q1-Q3]\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, while the number of pneumonia cases was associated with the minimum temperature at a lag of 2 days, the minimum temperature at a lag of 1 day (RR: 0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.973\u0026ndash;0.996) was found to be significantly associated with an increase in pneumonia cases (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A decrease in temperatures at lag 1 led to an increase in pneumonia cases. On the other hand, the effects of meteorological factors in male and female groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, while their effects across age groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of generalized additive Poisson models for predicting the number of admission \u0026hellip;. disease polyclinic (stratified by sex (gender))\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eRR (CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003eRR (CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003eRR (CI 95%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp - lag0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.005 (0.991\u0026ndash;1.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.002 (0.978\u0026ndash;1.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.003 (0.993\u0026ndash;1.014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp \u0026ndash; lag1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.006 (0.991\u0026ndash;1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.008 (0.993\u0026ndash;1.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.007 (0.996\u0026ndash;1.018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp \u0026ndash; lag2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.010 (0.995\u0026ndash;1.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.005 (0.990\u0026ndash;1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.008 (0.997\u0026ndash;1.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp \u0026ndash; lag3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.994 (0.981\u0026ndash;1.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.996 (0.983\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.995 (0.986\u0026ndash;1.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.999 (0.985\u0026ndash;1.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.007 (0.992\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.003 (0.992\u0026ndash;1.013)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.973 (0.957\u0026ndash;0.989)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.995 (0.979\u0026ndash;1.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.984 (0.973\u0026ndash;0.996)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.016 (1.001\u0026ndash;1.031)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.005 (0.991\u0026ndash;1.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.011 (1.001\u0026ndash;1.022)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.998 (0.985\u0026ndash;1.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.001 (0.987\u0026ndash;1.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.999 (0.990\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eRR, Relative Risk, bold p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of generalized additive Poisson models for predicting the number of admission \u0026hellip;. disease polyclinic (stratified by age)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003cp\u003eRR (CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003cp\u003eRR (CI 95%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp - lag0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e1,003 (0,990-1,015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,005 (0,988-1,023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp \u0026ndash; lag1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e1,005 (0,992-1,019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,010 (0,992-1,029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp \u0026ndash; lag2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e1,001 (0,987-1,014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1,023 (1,004\u0026thinsp;\u0026minus;\u0026thinsp;1,041)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Temp \u0026ndash; lag3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e1,000 (0,989-1,012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0,985 (0,969-1,001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e1,009 (0,996-1,022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,992 (0,974-1,010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0,981 (0,967-0,995)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,989 (0,970-1,008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e1,004 (0,991-1,017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1,024 (1,006\u0026thinsp;\u0026minus;\u0026thinsp;1,042)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin Temp \u0026ndash; lag3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e1,004 (0,993-1,016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,990 (0,974-1,006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u0026bull; \u003cem\u003eRR, Relative Risk, bold p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eXXX, a coastal city in the Eastern Mediterranean with a typical Mediterranean climate and notable socioeconomic diversity, can serve as a representative model for other similar regions experiencing climate change. Our study demonstrates a statistically significant association between cold temperatures and increased pneumonia incidence, with the most pronounced effect observed one day after cold exposure. This finding aligns with previous research indicating that abrupt temperature drops can increase susceptibility to respiratory infections by compromising mucosal defenses and enhancing viral replication(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Additionally, our results are consistent with studies from similar climatic regions, suggesting that cold spells may act as a trigger for pneumonia exacerbations, particularly among vulnerable populations such as the elderly and those with pre-existing lung diseases(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, our analysis reveals that while the effect of cold temperatures was statistically significant on the next day (lag 1), a similar but less pronounced effect was also noted on the day after next day (lag 2), suggesting a potential impact of cold exposure on pneumonia incidence. This delayed effect could be attributed to a time-dependent inflammatory response or a gradual change in ambient temperature, further emphasizing the need for timely public health interventions during cold spells. Such findings are supported by several studies highlighting the delayed effects of extreme weather events on respiratory infection rates(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimilar to residential buildings, institutions such as hospitals have their own internal ventilation systems designed to mitigate the impact of extreme cold and heat(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Additionally, due to frequent disinfection procedures and the more common use of antibiotics, the microbiota within hospitals differs significantly from that of the external environment(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Hospital-acquired pneumonias (HAPs) are a leading cause of nosocomial infections and represent the most common cause of nosocomial infection-related mortality(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In our study, we chose to focus on community-acquired pneumonia (CAP) in outpatients, as hospital-acquired pneumonias tend to be more localized issues, whereas climate change and related temperature fluctuations are more likely to impact the incidence of CAP. Focusing on CAP cases diagnosed exclusively by pulmonologists enhanced the diagnostic accuracy and reduced misclassification bias, which is a common limitation in studies relying solely on administrative data. This specificity strengthens the internal validity of our findings but may limit comparability with studies including hospitalized cases.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eOur study has certain limitations that warrant consideration. Our study utilized data from 2024, considering the impact of the COVID-19 pandemic in 2020\u0026ndash;2021 and the disruptions caused by the February 6, 2023 earthquake in the region. The long curfews imposed during the pandemic changed the epidemiological and even environmental data during this period. The significant disruptions in health and public services after the earthquake made it difficult to obtain reliable health and meteorological data from these periods. Consequently, we focused on short-term temperature effects, using daily data with a lag period of up to three days to capture the commonly reported 2- to 3-day incubation period for pneumonia(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, this approach may not fully reflect the cumulative or extended impact of prolonged cold spells on pneumonia incidence. Previous studies have suggested that sustained exposure to low temperatures or recurrent cold events can exacerbate respiratory vulnerability, particularly in high-risk populations such as older adults and children(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, while our study focused primarily on the impact of temperature on pneumonia incidence, other environmental factors such as humidity, air pollution, and wind speed were not included in the analysis for the reasons stated above. These variables can independently or synergistically affect respiratory infection risks and may modify the temperature-pneumonia relationship(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In order to generalize the results obtained by data collection with our method, more studies with longer periods and more parameters are needed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate a significant association between lower temperatures and increased pneumonia incidence, particularly evident one and two days following cold weather exposure. This underscores the importance of implementing targeted public health interventions, such as early warning systems and community outreach programs, to mitigate the impact of cold spells on respiratory health, especially in vulnerable populations such as the elderly and those with pre-existing lung conditions. Furthermore, our study highlights the necessity for region-specific analyses in understanding the localized health effects of climate change, as climatic and socio-demographic differences may substantially influence the observed associations.\u003c/p\u003e\u003cp\u003eDespite our findings, it is important to acknowledge discrepancies with previous literature, which may be attributable to methodological differences, including varying definitions of exposure, outcome measurement, and analytical approaches. Additionally, while advancements in health information systems have improved data accessibility and integration, epidemiological studies should maintain a strong connection with field data to ensure accurate case ascertainment and context-specific insights. With the increasing use of artificial intelligence in epidemiology, researchers may rely more on desk-based data analysis, potentially moving away from direct field data collection. Future studies should aim to maintain a balance between advanced data analysis and on-the-ground insights to provide a more complete picture of public health impacts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Hatay Mustafa Kemal University (Approval No: 63, dated January 8, 2025). Given the retrospective and anonymized nature of outpatient data, individual informed consent was waived. The study was conducted in accordance with the ethical standards of the institutional research committee and the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not contain any individual person\u0026rsquo;s data in any form\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAviılability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization was performed by KBA, DPY, MK. Data collection provided by KBA, MSO, HY\u0026Ouml;, SN, AK, BNE. Data analysis and interpretation was performed by MK. Manuscript was prepared by KBA, DPY. Project administration by KBA. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Turkish Meteorological Service for their unconditional support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJones BE, Ramirez JA, Oren E, Soni NJ, Sullivan LR, Restrepo MI, et al. Diagnosis and Management of Community-acquired Pneumonia. An Official American Thoracic Society Clinical Practice Guideline. Am J Respir Crit Care Med. 2025 Jul 18. doi: 10.1164/rccm.202507-1692ST.\u003c/li\u003e\n\u003cli\u003ePirkle LT, Jennings N, Vercammen A, Lawrance EL. Current understanding of the impact of climate change on mental health within UK parliament. Front Public Health. 2022 Sep 16;10:913857. \u003c/li\u003e\n\u003cli\u003eMartins FP, Paschoalotto MAC, Closs J, Bukowski M, Veras MM. The Double Burden: Climate Change Challenges for Health Systems. EnvironHealthInsights. 2024 Jan;18:11786302241298789. \u003c/li\u003e\n\u003cli\u003eMakrufardi F, Triasih R, Nurnaningsih N, Chung KF, Lin SC, Chuang HC. Extreme temperatures increase the risk of pediatric pneumonia: a systematic review and meta-analysis. Front Pediatr. 2024;12:1329918. \u003c/li\u003e\n\u003cli\u003eLee H, Yoon HY. Impact of ambient temperature on respiratory disease: a case-crossover study in Seoul. Respir Res. 2024 Feb 5;25(1):73. \u003c/li\u003e\n\u003cli\u003eZhu Z, Ji B, Tian J, Yin P. Heat exposure and respiratory diseases health outcomes: An umbrella review. Sci Total Environ. 2025 Mar 20;970:179052. \u003c/li\u003e\n\u003cli\u003eWu J, Wu Y, Wu Y, Yang R, Yu H, Wen B, et al. The impact of heat waves and cold spells on pneumonia risk: A nationwide study. Environmental Research. 2024 Mar;245:117958. \u003c/li\u003e\n\u003cli\u003eMiyayo SF, Owili PO, Muga MA, Lin TH. Analysis of Pneumonia Occurrence in Relation to Climate Change in Tanga, Tanzania. IJERPH. 2021 Apr 29;18(9):4731. \u003c/li\u003e\n\u003cli\u003ePedder H, Kapwata T, Howard G, Naidoo RN, Kunene Z, Morris RW, et al. Lagged Association between Climate Variables and Hospital Admissions for Pneumonia in South Africa. IJERPH. 2021 Jun 8;18(12):6191. \u003c/li\u003e\n\u003cli\u003eMotlogeloa O, Fitchett JM. Assessing the impact of climatic variability on acute respiratory diseases across diverse climatic zones in South Africa. Science of The Total Environment. 2024 Mar;918:170661. \u003c/li\u003e\n\u003cli\u003eHe Q, Liu Y, Yin P, Gao Y, Kan H, Zhou M, et al. Differentiating the impacts of ambient temperature on pneumonia mortality of various infectious causes: a nationwide, individual-level, case-crossover study. eBioMedicine. 2023 Dec;98:104854. \u003c/li\u003e\n\u003cli\u003eSohn S, Cho W, Kim JA, Altaluoni A, Hong K, Chun BC. \u0026lsquo;Pneumonia Weather\u0026rsquo;: Short-term Effects of Meteorological Factors on Emergency Room Visits Due to Pneumonia in Seoul, Korea. J Prev Med Public Health. 2019 Mar 31;52(2):82\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eHuang D, Taha MS, Nocera AL, Workman AD, Amiji MM, Bleier BS. Cold exposure impairs extracellular vesicle swarm\u0026ndash;mediated nasal antiviral immunity. Journal of Allergy and Clinical Immunology. 2023 Feb;151(2):509-525.e8. \u003c/li\u003e\n\u003cli\u003eWang Z, Zhou Y, Luo M, Yang H, Xiao S, Huang X, et al. Association of diurnal temperature range with daily hospitalization for exacerbation of chronic respiratory diseases in 21 cities, China. Respir Res. 2020 Dec;21(1):251. \u003c/li\u003e\n\u003cli\u003ePutot A, Garin N, Rello J, Prendki V. Comprehensive management of pneumonia in older patients. European Journal of Internal Medicine. 2025 May;135:14\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eGuo W, Yi L, Wang P, Wang B, Li M. The effect of air temperature on hospital admission of adults with community acquired pneumonia in Baotou, China. Sci Rep. 2021 Apr 30;11(1):9353. \u003c/li\u003e\n\u003cli\u003eLane MA, Walawender M, Brownsword EA, Pu S, Saikawa E, Kraft CS, et al. The impact of cold weather on respiratory morbidity at Emory Healthcare in Atlanta. Science of The Total Environment. 2022 Mar;813:152612. \u003c/li\u003e\n\u003cli\u003eAchebak H, Garcia-Aymerich J, Rey G, Chen Z, M\u0026eacute;ndez-Turrubiates RF, Ballester J. Ambient temperature and seasonal variation in inpatient mortality from respiratory diseases: a retrospective observational study. The Lancet Regional Health - Europe. 2023 Dec;35:100757. \u003c/li\u003e\n\u003cli\u003eRen C, Wang J, Feng Z, Kim MK, Haghighat F, Cao SJ. Refined design of ventilation systems to mitigate infection risk in hospital wards: Perspective from ventilation openings setting. Environmental Pollution. 2023 Sep;333:122025. \u003c/li\u003e\n\u003cli\u003eNourozi B, Wierzbicka A, Yao R, Sadrizadeh S. A systematic review of ventilation solutions for hospital wards: Addressing cross-infection and patient safety. Building and Environment. 2024 Jan;247:110954. \u003c/li\u003e\n\u003cli\u003eDuller S, Kumpitsch C, Moissl-Eichinger C, Wink L, Koskinen Mora K, Mahnert A. In-hospital areas with distinct maintenance and staff/patient traffic have specific microbiome profiles, functions, and resistomes. Gibbons JG, editor. mSystems. 2024 Aug 20;9(8):e00726-24. \u003c/li\u003e\n\u003cli\u003eJean SS, Chang YC, Lin WC, Lee WS, Hsueh PR, Hsu CW. Epidemiology, Treatment, and Prevention of Nosocomial Bacterial Pneumonia. JCM. 2020 Jan 19;9(1):275. \u003c/li\u003e\n\u003cli\u003eCandel FJ, Salavert M, Estella A, Ferrer M, Ferrer R, Gamazo JJ, et al. Ten Issues to Update in Nosocomial or Hospital-Acquired Pneumonia: An Expert Review. J Clin Med. 2023 Oct 14;12(20):6526. \u003c/li\u003e\n\u003cli\u003ePahal, Priyanka Rajasurya, Vipin Nguyen, Andrew D. (son). Typical Bacterial Pneumonia. In: StatPearls [Internet] [Internet]. Jan 2025-. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 May 10]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK534295/\u003c/li\u003e\n\u003cli\u003eReis Da Silva TH. The impact of cold weather on older people and the vital role of community nurses. Br J Community Nurs. 2025 Jan 2;30(1):28\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eMonoson A, Schott E, Ard K, Kilburg-Basnyat B, Tighe RM, Pannu S, et al. Air pollution and respiratory infections: the past, present, and future. Toxicological Sciences. 2023 Mar 20;192(1):3\u0026ndash;14. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"community-acquired pneumonia, cold spells, climate change and health","lastPublishedDoi":"10.21203/rs.3.rs-7515402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7515402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eClimate change, characterized by extreme temperature fluctuations, has emerged as a significant risk factor for respiratory diseases, including pneumonia. Most previous studies have focused on hospital-acquired pneumonia or ICD-based data, which may have led to misclassification. This study aimed to evaluate the impact of extreme temperature events, particularly cold spells, on the incidence of community-acquired pneumonia in XXX, a coastal city in the Mediterranean region.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMeteorological data were obtained from the XXX Meteorology Station, and pneumonia cases diagnosed by pulmonologists were collected from six hospitals between January 2 and December 30, 2024. Generalized Additive Poisson Regression Models were applied to assess the lag effects (lag 0\u0026ndash;3 days) of maximum and minimum temperatures on pneumonia incidence, adjusting for long-term trends and seasonality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 13,651 pneumonia cases were recorded, of whom 51.5% (n\u0026thinsp;=\u0026thinsp;7034) were male. Minimum temperature at lag 1 day was significantly associated with an increased risk of pneumonia (RR: 0.984, 95% CI: 0.973\u0026ndash;0.996, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the effect being more pronounced among the elderly (\u0026gt;\u0026thinsp;65 years).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eExposure to cold temperatures was associated with an increased incidence of pneumonia, particularly one day after exposure. These findings highlight the need for targeted public health interventions during cold spells, especially for vulnerable populations. Considering the longer incubation periods of pathogenic microbiomes, this association may be linked to the increased virulence of colonizing microorganisms triggered by cold weather conditions.\u003c/p\u003e","manuscriptTitle":"Impact of Cold Spells on Community-Acquired Pneumonia Incidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 17:23:44","doi":"10.21203/rs.3.rs-7515402/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5373eea0-377c-4217-87e3-0e3cc66aa499","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T12:25:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 17:23:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7515402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7515402","identity":"rs-7515402","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}