Socio environmental determinants of cardiovascular mortality and hospitalization risk in arid and semi-arid regions – a case study for Gonabad city,Iran 2017-2022

Socio environmental determinants of cardiovascular mortality and hospitalization risk in arid and semi-arid regions – a case study for Gonabad city,Iran 2017-2022 | 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 Socio environmental determinants of cardiovascular mortality and hospitalization risk in arid and semi-arid regions – a case study for Gonabad city,Iran 2017-2022 Ali Mohamadpour, Zahed Rezaei, Arash Parvari, Susana Rodriguez-Couto, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4086774/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 Since questioning regarding climate and incidents of cardiovascular rates have been debated. This study was designed to investigate the association between cardiovascular mortality hospitalization risk and demographic-meteorological factors in the arid and semi-arid zone of Iran during (1st April 2017 and 31st December 2022). Methods Logistic and negative binomial regression and Pearson regression were used for analysis. Results A mean age of 61.52 years old (49.5% female and 50.5% male) was recorded for the hospitalization. Peak numbers of daily hospitalization were observed in winter (18%), followed by autumn (16%). Cardiovascular hospitalization presented a significant positive correlation with the wind (P = 0.05) and temperature (P = 0.016) in the hot season, whereas showing a negative correlation with humidity (P = 0.013) and wind factor (P = 0.05) in the cold season. Similarly, a negative correlation between cardiovascular mortality and speed of wind (P = 0.05) was observed in summer. Conclusion It concluded that there were associations between demographic-meteorological factors and the occurrence of cardiovascular hospitalization-mortality in arid and semiarid region. Iran Environmental Cardiovascular hospitalization mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In recent decades, climate change has led to changes in the frequency and intensity of the temperature, pressure, and wind speed globally. Climate underlying data clearly show a warming trend in many parts of the world in the last few years. Moreover, unfavorable atmospheric situations cause adverse health effects worldwide [ 1 ]. Climate stress, such as ambient temperature, atmospheric pressure, and wind speed, is one of the biggest global health threats associated with various mortality risks in the 21st century [ 2 ]. Similary, atmospheric data shows the variation of seasonal patterns from region to region. It is demonstrated that seasonal patterns such as ambient temperature, atmospheric pressure, and wind rate are the most highlighted patterns that are closely linked to the regional situation [ 3 ]. According to meta-regression study reports, atmospheric seasonal parameters were directly linked to adverse health effects in regions with a climate phenomenal situation such as high rate of wind and temperature globally [ 4 ]. In Iran, climate phenomena can be seen frequently. In addition, some adverse health trends have been documented in association with climate conditions recently in Iran. Recent documents reported that daily atmospheric parameter changes, based on temperature, humidity, and wind, are directly linked to cardiovascular mortality and hospitalization risk. Similarly, it was demonstrated that demographical variables such as age are the most important cardiovascular risk factor (CRF) that lead to several pathophysiological changes [ 5 ]. Accordingly, the United Nations Framework Convention on Climate Change (UNFCCC) data, global warming is addressed simply by increasing the number of cardiovascular, respiratory, and other diseases [ 6 ]. According to reported data, cardiovascular diseases (CVDs) are one of the most common causes of Iranian death [ 7 ]. Cardiac diseases have been specified with the variability of patient characteristics such as age, gender, body weight, medical history, family history, smoking behavior, and activity before the arrest. However, the role of environmental factors in CVDs is less known, and differences in available records were observed [ 8 ]. While most authors describe an inverse effect [ 9 , 10 ],, others suggested a direct association with mortality and morbidity during high and low ambient temperatures, atmospheric pressure, and wind speed [ 11 , 12 ]. The study carried out in Scotland showed that the mortality trend had a steeper increase at lower temperatures than at warmer temperatures [ 13 ]. In addition, another study reported lower numbers of CVDs hospitalizations during the warm season [ 14 ]. Moreover, different studies also reported that decreasing temperatures led to increasing vasopressin levels [ 15 ]. However, the outcomes of time series studies showed a minimum of 24 hours between the decrease in temperature and an increase in acute coronary syndrome (ACS) mortality [ 16 ]. Another study reported that CVDs mortality associated with temperature in the elderly subgroup was stronger than in younger age groups [ 17 ]. Even though most previous studies have considered only temperature as a meteorological factor, it seems wind speed factor may be a better predictor of mortality than temperature alone [ 18 ]. In Iran, there are different studies on the associated air pollution with CVDs but there are few studies associating the atmospheric factors (pressure, humidity, wind, and sunlight) with CVDs particularly in arid regions such as Gonabad city. To our knowledge, there is no study investigating the role of environmental stress in Gonabad. Thus, this is the first study that investigates the association between environmental factors and CVDs mortality in Gonabad city. Material and Methods Study area Gonabad city has been located in the south of Razavi Khorasan province with an area of nearly 105801 square km and 265 km away from Mashhad (the center of Razavi Khorasan province). The city has been located in an arid and semi-arid zone on the edge of the Lut desert (Figure.1). According to Gonabad synoptic stations, the prevailing wind direction is from the southeast (SE) to the northeast (NE). Data collection Ethical approval for this study was obtained from Gonabad University of Medical Sciences (IR.GMU.REC.1400.197). Additionally, t otal CVDs data were collected between 1st April 2017 and 31st December 2022 from Bohlol Gonabadi Hospital of Gonabad city. In addition, the meteorology data (temperature, humidity, wind speed, and pressure) on daily maximum and minimum were obtained from Gonabad Bureau Meteorology. Statistical analysis Firstly, mean monthly, seasonal, and annual CVDs mortality-hospitalization were calculated. Next, Meteorological data and CVDs mortality-hospitalization were matched with a time shift. Finally, the association between meteorological variability with CVDs mortality-hospitalization rate was investigated. Spring data was used as a reference to compare other seasons. Data were analyzed via Pearson correlation coefficients and negative binomial regression analysis. The effect of metrological factors on the cardiovascular hospitalization and mortality was first studied by Pearson regression for dependent count data. However, due to over-dispersion and lack of fitness with Pearson regression (according to statistical tests), negative binomial regression was used instead. Data were analyzed by using Stata software (version 17). Results Descriptive statistics Table 1 provides descriptive statistics of meteorological and CVDs demographics characteristics (mean, standard deviation, minimum, and maximum) for each season during the studied period and weather conditions. According to statistical analyses, the Mean ± SD was 61.52‌±20.13 years old. However, 49.4% of the subjects were female. In addition, the mean age mortality and hospitalization were 76.8 and 60.7 years old, respectively. The mean weather temperature was 10.55 ± 6.13°C and 25.67 ± 5.58°C in cold and warm seasons, respectively, with intraday variations of up to 15°C. The cold season (lasts from October to March) and the warm season (runs from April to September) were classified. Morever, Table 1 shows no association between gender and CVDs mortality, while the strongest association is observed between age and CVDs mortality (P = 0.001). According to the obtained data CVDs, mortality increases by increasing each population year old [OR; 1.06 95% CI 1.05 to 1.07]. Table 1 Climate and characteristics of CVDs according Logistic regression Recovery Mortality Total (95%CI) Demographics (n = 6529) Age (Mean ± SD) 60.78 ± 20.03 76.85 ± 15.68 61.52‌±20.13 Gender Female ( N, % ) 3139 (49.4) 160 (52.1) 3299 (49.5) Male ( N, % ) 3218 (50.6) 147 (47.9) 3356 (50.5) Meteorological exposures Average, Temperature Cold season 10.60 ± 6.20 10.31 ± 5.76 10.55 ± 6.13 Warm season 25.66 ± 5.54 25.73 ± 5.80 25.67 ± 5.58 Average, Humidity Cold season 46.43 ± 18.77 44.98 ± 18.18 16.19 ± 18.67 Warm season 28.60 ± 15.99 28.69 ± 17.02 28.61 ± 16.14 Average pressure Cold season 896.20 ± 4.02 896.34 ± 3.56 896.19 ± 3.96 Warm season 890.48 ± 3.31 890.11 ± 3.44 890.43 ± 3.33 Average wind Cold season 5.39 ± 3.12 5.21 ± 3.59 5.36 ± 3.21 Warm season 7.68 ± 2.82 7.96 ± 3.16 7.72 ± 2.88 Association between CVDs hospitalization and meteorological factors The association between ambient temperature, relative air humidity, atmospheric pressure, and wind speed with the probability of CVDs hospitalization occurrence is shown in Table 2 . As can be seen, the incidence of emergency CVDs hospitalizations was significantly associated with humidity during the autumn season (P = 0.01). Although, the temperature was also significantly associated with CVDs in the summer season (P = 0.01). Additionally, Pearson correlations between hospitalization and wind speed factor showed positive correlations (P = 0.05) in the summer and autumn seasons. Moreover, hospitalization varied seasonally with the peak number of hospitalization in winter followed by autumn (Fig. 2 ). The cumulative percentiles of hospitalization counts are illustrated in Fig. 3. However, it was found that the peak number of daily hospitalizations was three persons (18%) followed by two persons (16%). Table 2 Association CVDs hospitalization-mortality with meteorological factors according Pearson regression Seasons Coefficient Std. Err P-Value Adjusted coefficient (95% confidence interval) Hospitalization Humidity Spring Summer 0.00 0.98 0.01 0.02 0.83 0.55 [-0.01 to 0.02] [-0.07 to 0.03] Autumn 0.99 0.01 0.76 [-0.02 to 0.02 ] Winter 0.99 0.01 0.56 [-0.03 to 0.01] Spring -0.04 0.02 0.13 [ -0.1 to 0.1 ] Wind Summer 1.13 0.06 0.05* [− 0.26 to 0.00] Autumn 0.98 0.04 0.67 [ 0.10 to 0.07] Winter 0.92 0.05 0.14 [− 0.17 to 0.02] Spring -0.01 0.03 0.6 [-0.09 to 0.06] Temperature Summer 0.95 0.07 0.55 [− 0.10 to 0.18] Autumn 1.03 0.04 0.43 [-0.12 to 0.05] Winter 1.04 0.05 0.39 [-0.05 to 0.14] Spring -0.02 0.04 0.6 [ -0.1 to 0.06 ] Pressure Summer 0.70 0.07 0.64 [ -0.18 to 0.11] Autumn 0.93 0.06 0.30 [ -0.18 to 0.05] Winter 0.92 0.05 0.14 [ -2.08 to -1.62] Spring 0.00 0.01 0.8 [-0.01 to 0.02] Mortality Humidity Summer -0.01 0.02 0.55 [ -0.18 to 0.00] Autumn -0.00 0.01 0.01* [-0.00 to 0.01] Winter -0.00 0.01 0.23 [-0.00 to 0.01 ] Spring 0.04 0.02 0.01 [ -0.01 to 0.1] Wind Summer 0.97 0.01 0.05* [-0.06 to 0.00] Autumn 0.97 0.01 0.05* [-0.05 to 0.00] Winter 0.98 0.01 0.29 [-0.05 to 0.01] Spring -0.01 0.04 0.60 [ -0.09 to 0.06] Temperature Summer 1.03 0.01 0.01* [-0.00 to 0.07] Autumn 1.01 0.01 0.22 [-0.01 to 0.04] Winter 1 0.01 0.50 [-0.03 to 0.01] Spring -0.02 0.04 0.61 [ -0.01 to 0.1] Pressure Summer 1.02 0.02 0.32 [-0.02 to 0.06] Autumn 0.99 0.01 0.84 [-0.03 to 0.03] Winter 0.98 0.01 0.22 [− 0.05 to 0.01] Fiqure 3. Cumulative percentiles of hospitalization counts were determined Association between CVDs mortality and meteorological factors The association between ambient temperature, air humidity, atmospheric pressure, wind speed, and CVDs mortality occurrence probability is shown in Table 2 . The data show a positive association between CVDs mortality and wind factor in the warm season more than in the cold season. Figure 4 presents the overall trend of CVDs mortality during various seasons over time. The cumulative trend of the data illustrated that the autumn season experienced a higher level of daily mortality in comparison with other seasons. Irrespective of that, the summer season showed the highest daily no-mortality counts. Moreover, according to the obtained data, nearly 87% of the days were recorded as non-mortality days during the studied period (Figure. 5). Discussion The current study presents that cardiovascular hospitalization was strongly linked to environmental factors and seasonal change. According to the World Health Organization Countdown, negative climate conditions have serious implications for public health including cardiac health (19). This study illustrated a seasonal variable hospitalization by using more than > 6529. The data showed an increase in daily CVDs hospitalization's number in the cold season. The highest number (4 people) of daily CVDs hospitalization (4.86%) strongly coincided with the winter followed by the autumn seasons. While the lowest number occurred in the spring season. It is undeniable that seasonal changes lead to behavioral pattern variations such as changes in diet, physical activity, and psychosocial factors that are considered key factors in high CVDs incidence [ 19 ]. Moreover, it was suggested that cold-induced systemic hypertension and pulmonary hypertension were some of the most important risk factors that are leads to the incidence of CVDs hospitalizations in the cold season [ 20 ]. Previous evidence declared that luminal diameter significantly decreased after 1 min of cold-pressor stimulation. This may be why a lower temperature range is the main environmental factor leading to higher incidences of hospitalization in cardiology wards [ 21 ]. According to the findings of this study, there exists a significant association between lower humidity and CVDs hospitalization (P = 0.013 [Coefficient − 0.00 [-0.00 to 0.01]) in the autumn season. However, cold and low humidity led to an increase in CVDs hospitalization incidences. The results obtained by Lee et al. support our results [ 22 ]. In contrast, Panagiotakos et al. reported a positive correlation between humidity and hospital admission [ 23 ]. Swartz et al. mentioned that there was no evidence of CVDs hospitalization and humidity effect [ 24 ]. Although there is no clear evidence of the exact humidity mechanism on the CVDs accuracy, some scientists believe that high humidity weather interferes with the processes of perspiration and body temperature homeostasis, resulting in respiratory fatigue and increased heart rate [ 25 ]. From this point of view, very low humidity and low temperatures can intensify the effect of each other and increase the incidence of respiratory diseases and influenza, which, in turn, can increase CVDs incidence [ 26 ]. Moreover, hospitalization outcomes risk in both cold (autumn) and warm (summer) season was associated with wind speed positively (P = 0.05 (Coefficient, 0.03 [-0.06 to 0.00]) and negatively (Coefficient, − 0.02 [-0.05 to 0.00], respectively. The inverse relationship between wind speed and CVDs hospitalization has been well-documented in previous literature [ 27 – 29 ]. In this case, it seems that complex topography and strong tropical seasonal wind in Kavir and Lut deserts are directly responsible for the transport of pollutants in the large-scale area. Interestingly, in the summer higher temperatures and air inversion can intensify the increasing healthy effect of tropical wind and, therefore, hospitalization [ 30 ]. Based on our results, the number of CVDs hospitalizations incidents was strongly associated with higher ambient temperature (coefficient, 0.036 [-0.00 to 0.07] per ℃, P = 0.016) in the summer season. The role of environmental temperature is conflicting so the approach is debatable. The effect of hot temperatures on emergency hospital admissions for CVDs was reported widely in different climate situation regions [ 30 – 32 ]. Harvesting and agricultural activities intensify the effect of hot temperatures in arid and semi-arid regions. This phenomenon causes stressor effects, mainly on a pool of frail individuals, which leads to more heart attacks and CVDs hospitalization [ 33 ]. Moreover, the scientific explanation of the effect of temperature on the increasing CVDs hospitalization is that heat damages the structure of endothelial cells in coronary arteries and increases the permeability of the tunica intima. Reduces the superoxide dismutase (SOD) activity of heart tissues and finally increases the lipoproteins in the oxygenated blood [ 34 ]. As a result, a large amount of cholesterol settled on the tunica intima causing atherosclerosis and exacerbating coronary heart disease. However, our data showed a positive correlation between wind speed and CVDs mortality (P = 0.057) in summer. Nevertheless, CVDs mortality increased with decreasing wind speed [Coefficient, -0. 13 (− 0.26 to 0.00)]. Conclusion The arid and semi-arid region's population suffers from various environmental problems in Iran that are rooted in tough weather conditions. To the best of our knowledge, this study represents the first attempt to do so in an arid city with variable and specific weather conditions. The scenario under analysis estimated that the mean monthly hospitalization in the cold seasons is higher than in the hot seasons. In addition, according to the obtained data, CVDs hospitalization has a significant positive association with wind and temperature factors in the hot season while a negative association with humidity and wind in the cold season was observed. Similarly, it shows a negative association between CVDs mortality and wind speed in summer. Overall, this study strongly suggests taking action against sudden climate phenomena in arid and semi-arid regions (with complex weather) for the protection of the vulnerable population and prevention of CVDs incidents Declarations Acknowlagements The authors would like to appreciate the Social determining Health Research Center Gonabad university of medical science for supporting. for their helpful comments on the manuscript . Author contributions Laleh R. Kalankesh, Ali Mohammadpour, and Ali Alami designed the stud Zahed rezaei and Arash Prvari analyzed and modeled the data. Laleh R.Kalankesh was writing the paper. Susana Rodriguez-Couto and shahla khosravan edit and approved the final manuscript. Funding None. Data availability Data is provided within the manuscript. Competing interests The authors declare no competing interests . Ethics approval and consent to participate We confirm that all methods were carried out in accordance with relevant guidelines and regulations. Ethics committee of Gonabad university of Medical science waived the need for informed consent based on the study’s retrospective analysis of patient data. All experimental protocols of this study were approved by the ethics committee of Gonabad university of Medical science, Iran (IR.GMU.REC.1400.197). Consent for publication Not Applicable . Author details 1 Social Determinants of Health Research Center, Gonabad University of Medical sciences, Gonabad, Iran 2 Faculty of Health, Tehran University of Medical Science, Tehran, Iran 3 Department of Separation Science, LUT School of Engineering Science, LUT University, Sammonkatu 12, 50130 Mikkeli, Finland References Parker, E.R., The influence of climate change on skin cancer incidence – A review of the evidence. International Journal of Women's Dermatology, 2021. 7; 1. 17-27. Grigorieva, E.A. and B.A. Revich, Health Risks to the Russian Population from Temperature Extremes at the Beginning of the XXI Century. Atmosphere, 2021. 12; 10. 1331. Tian, G., Z. Qiao, and X. Xu, Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001–2012 in Beijing. Environmental pollution, 2014. 192. 266-274. D'Amato, G., et al., Meteorological conditions, climate change, new emerging factors, and asthma and related allergic disorders. A statement of the World Allergy Organization. World Allergy Organ J, 2015. 8; 1. 25-25. Mousavi, A., et al., Climate change and health in Iran: a narrative review. J Environ Health Sci Eng, 2020. 18; 1. 367-378. A'yun, I.Q. and U. Khasanah, The Impact of Economic Growth and Trade Openness on Environmental Degradation: Evidence from A Panel of ASEAN Countries. Jurnal Ekonomi & Studi Pembangunan, 2022. 23; 1. 81-92. Sarrafzadegan, N. and N. Mohammadifard, Cardiovascular disease in Iran in the last 40 years: prevalence, mortality, morbidity, challenges and strategies for cardiovascular prevention. Arch Iran Med 2019. 22; 4. 204-210. Bemanalizadeh, M., Z. Farajzadegan, and P. Golshiri, Estimation of Cardiovascular Disease Risk Factors in the Undefined Participants of Campaign in Isfahan in 2017. Int J Prev Med, 2021. 12. 47-47. Dadbakhsh, M., N. Khanjani, and A. Bahrampour, The relation between mortality from cardiovascular diseases and temperature in Shiraz, Iran, 2006-2012. ARYA Atheroscler, 2018. 14; 4. 149-156. Moghadamnia, M.T., et al., The effects of apparent temperature on cardiovascular mortality using a distributed lag nonlinear model analysis: 2005 to 2014. Asia Pacific Journal of Public Health, 2018. 30; 4. 361-368. Moghadamnia, M.T., et al., Ambient temperature and cardiovascular mortality: a systematic review and meta-analysis. PeerJ, 2017. 5. e3574-e3574. Huang, H., et al., Spatio-temporal mechanism underlying the effect of urban heat island on cardiovascular diseases. Iranian journal of public health, 2020. 49; 8. 1455. Carder, M., et al., The lagged effect of cold temperature and wind chill on cardiorespiratory mortality in Scotland. Occupational and environmental medicine, 2005. 62; 10. 702-710. Boussoussou, N., et al., Complex effects of atmospheric parameters on acute cardiovascular diseases and major cardiovascular risk factors: data from the Cardiometeorology(SM) study. Sci Rep, 2019. 9; 1. 6358-6358. Campbell, G.A. and M.H. Rosner, The agony of ecstasy: MDMA (3, 4-methylenedioxymethamphetamine) and the kidney. Clinical Journal of the American Society of Nephrology, 2008. 3; 6. 1852-1860. Li, Y., et al., Association between high temperature and mortality in metropolitan areas of four cities in various climatic zones in China: a time-series study. Environmental Health, 2014. 13; 1. 1-10. Rocklöv, J., et al., Susceptibility to mortality related to temperature and heat and cold wave duration in the population of Stockholm County, Sweden. Global health action, 2014. 7; 1. 22737. Mohan, M., A. Gupta, and S. Bhati, A modified approach to analyze thermal comfort classification. Atmospheric and Climate Sciences, 2014. 4; 1. 7-19. Dennison, R.A., et al., The association between psychosocial factors and change in lifestyle behaviour following lifestyle advice and information about cardiovascular disease risk. BMC Public Health, 2018. 18; 1. 731-731. Fares, A., Winter cardiovascular diseases phenomenon. N Am J Med Sci, 2013. 5; 4. 266-279. Yoneyama, K., et al., Weather temperature and the incidence of hospitalization for cardiovascular diseases in an aging society. Sci Rep, 2021. 11; 1. 1-11. Lee, J.H., et al., Influence of weather on daily hospital admissions for acute myocardial infarction (from the Korea Acute Myocardial Infarction Registry). International journal of cardiology, 2010. 144; 1. 16-21. Panagiotakos, D.B., et al., Climatological variations in daily hospital admissions for acute coronary syndromes. International journal of cardiology, 2004. 94; 2-3. 229-233. Schwartz, J., J.M. Samet, and J.A. Patz, Hospital admissions for heart disease: the effects of temperature and humidity. Epidemiology, 2004. 15; 6. 755-761. Higuma, T., et al., Effects of temperature and humidity on acute myocardial infarction hospitalization in a super-aging society. Sci Rep, 2021. 11; 1. 1-10. Zhang, R., et al., The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Respiratory Research, 2021. 22; 1. 153. Qiu, X., et al., Inverse probability weighted distributed lag effects of short-term exposure to PM2. 5 and ozone on CVD hospitalizations in New England Medicare participants-Exploring the causal effects. Environmental research, 2020. 182. 109095. Radišauskas, R., et al., Trends of myocardial infarction morbidity and its associations with weather conditions. Medicina, 2014. 50; 3. 182-189. Ogbomo, A.S., et al., Vulnerability to extreme-heat-associated hospitalization in three counties in Michigan, USA, 2000–2009. International journal of biometeorology, 2017. 61; 5. 833-843. Giang, P.N., et al., The effect of temperature on cardiovascular disease hospital admissions among elderly people in Thai Nguyen Province, Vietnam. Global health action, 2014. 7. 23649-23649. Li, M., et al., Impact of Extremely Hot Days on Emergency Department Visits for Cardiovascular Disease among Older Adults in New York State. Int J Environ Res Public Health, 2019. 16; 12. 2119. Zhai, G., et al., The effect of apparent temperature on hospital admissions for cardiovascular diseases in rural areas of Pingliang, China. Annals of Agricultural and Environmental Medicine, 2022. 29; 2. 281–286. Bell, J.E., et al., Ch. 4: Impacts of extreme events on human health , 2016, US Global Change Research Program, Washington, DC. p. 99-128. Favero, G., et al., Endothelium and its alterations in cardiovascular diseases: life style intervention. BioMed research international, 2014. 2014. 1-23. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4086774","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280678469,"identity":"217e1273-8af0-4c4d-913a-b78cc58591e8","order_by":0,"name":"Ali Mohamadpour","email":"","orcid":"","institution":"Gonabad University of Medical sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Mohamadpour","suffix":""},{"id":280678470,"identity":"d52c742b-709c-44df-b2f5-8424f913d656","order_by":1,"name":"Zahed Rezaei","email":"","orcid":"","institution":"Gonabad University of Medical sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahed","middleName":"","lastName":"Rezaei","suffix":""},{"id":280678471,"identity":"a716f2f9-62f2-4766-9e7c-39adae18e0a7","order_by":2,"name":"Arash Parvari","email":"","orcid":"","institution":"Tehran University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Arash","middleName":"","lastName":"Parvari","suffix":""},{"id":280678472,"identity":"846ab412-3171-4431-9ab3-96f47dc7dde3","order_by":3,"name":"Susana Rodriguez-Couto","email":"","orcid":"","institution":"LUT University","correspondingAuthor":false,"prefix":"","firstName":"Susana","middleName":"","lastName":"Rodriguez-Couto","suffix":""},{"id":280678473,"identity":"4f6f9b57-f43a-4e76-8608-dd9f3094077a","order_by":4,"name":"Ali Alami","email":"","orcid":"","institution":"Gonabad University of Medical sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Alami","suffix":""},{"id":280678474,"identity":"2fb59654-0176-4e42-bed9-9561be40eae7","order_by":5,"name":"Shahla khosravan","email":"","orcid":"","institution":"Gonabad University of Medical sciences","correspondingAuthor":false,"prefix":"","firstName":"Shahla","middleName":"","lastName":"khosravan","suffix":""},{"id":280678475,"identity":"0167bfcf-25ce-46a3-89dc-024573b74c60","order_by":6,"name":"Laleh R. Kalankesh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACCQbmBgaGAwwMBiAioQIoxAwSwauFEaqFEaTlDEgLI7FamIEEYxtIjIAWyfaDjZ8LztjYm7OdMXvwcF5tNH87UMuPim04tUjzJDZLz7iRlriz54y5QeK247kzDjM2MPacuY1TixxDYoM0z4fDCQY3zphJJG47ltsA1MLM2IZHC//D5t88H/7bG9x/A9Qy51jufEJapCUS26R5bhxg3HAAZEtDTe4GQlokZzxss+Y5k5y44cCxMomEYwdyNwK1HMTnF4nzyYdv8xyzszc4cHib5I+autx55w8ffPCjArcWdHAYTB4gWj0Q1JGieBSMglEwCkYIAAA5oWRnszD3hgAAAABJRU5ErkJggg==","orcid":"","institution":"Gonabad University of Medical sciences","correspondingAuthor":true,"prefix":"","firstName":"Laleh","middleName":"R.","lastName":"Kalankesh","suffix":""}],"badges":[],"createdAt":"2024-03-12 18:15:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4086774/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4086774/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53192179,"identity":"e0dc888d-ba47-4ead-804c-e9fc0995f969","added_by":"auto","created_at":"2024-03-21 17:46:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":190037,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Gonabad city in Iran.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4086774/v1/d60db1f7c87ed2eaab9db63c.png"},{"id":53192180,"identity":"d526ea8f-47ac-4357-95c9-0321f7b6d69e","added_by":"auto","created_at":"2024-03-21 17:46:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59011,"visible":true,"origin":"","legend":"\u003cp\u003eOverall daily hospitalization in various seasons (2016-2021) was determined.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4086774/v1/f2b1c4a7fde5c51752b4707e.png"},{"id":53192181,"identity":"382c87c0-67f7-43cb-86c6-a006f4c5fc6e","added_by":"auto","created_at":"2024-03-21 17:46:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121904,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative percentiles of hospitalization counts were determined\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4086774/v1/1989721f346bf51f13f7a7e7.png"},{"id":53192183,"identity":"480177ad-f74b-401c-a102-ae3d5d31d97d","added_by":"auto","created_at":"2024-03-21 17:46:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42347,"visible":true,"origin":"","legend":"\u003cp\u003eOverall daily mortality in various seasons were determined (2016-2021)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4086774/v1/8f823eb58adf13597559032e.png"},{"id":53192182,"identity":"8a5fb2bf-da77-4a9a-9847-3a02bc201846","added_by":"auto","created_at":"2024-03-21 17:46:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31465,"visible":true,"origin":"","legend":"\u003cp\u003eOverall percent counts of mortality for each day were determined\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4086774/v1/cd3556c8f415bf0789853f52.png"},{"id":63033535,"identity":"08bf2ead-a626-479b-8000-7812f1d95936","added_by":"auto","created_at":"2024-08-22 09:53:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1185153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4086774/v1/8bece530-a46c-4b6a-823b-e910d5a7b2cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socio environmental determinants of cardiovascular mortality and hospitalization risk in arid and semi-arid regions – a case study for Gonabad city,Iran 2017-2022","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, climate change has led to changes in the frequency and intensity of the temperature, pressure, and wind speed globally. Climate underlying data clearly show a warming trend in many parts of the world in the last few years. Moreover, unfavorable atmospheric situations cause adverse health effects worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Climate stress, such as ambient temperature, atmospheric pressure, and wind speed, is one of the biggest global health threats associated with various mortality risks in the 21st century [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Similary, atmospheric data shows the variation of seasonal patterns from region to region. It is demonstrated that seasonal patterns such as ambient temperature, atmospheric pressure, and wind rate are the most highlighted patterns that are closely linked to the regional situation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to meta-regression study reports, atmospheric seasonal parameters were directly linked to adverse health effects in regions with a climate phenomenal situation such as high rate of wind and temperature globally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Iran, climate phenomena can be seen frequently. In addition, some adverse health trends have been documented in association with climate conditions recently in Iran. Recent documents reported that daily atmospheric parameter changes, based on temperature, humidity, and wind, are directly linked to cardiovascular mortality and hospitalization risk. Similarly, it was demonstrated that demographical variables such as age are the most important cardiovascular risk factor (CRF) that lead to several pathophysiological changes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Accordingly, the United Nations Framework Convention on Climate Change (UNFCCC) data, global warming is addressed simply by increasing the number of cardiovascular, respiratory, and other diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to reported data, cardiovascular diseases (CVDs) are one of the most common causes of Iranian death [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Cardiac diseases have been specified with the variability of patient characteristics such as age, gender, body weight, medical history, family history, smoking behavior, and activity before the arrest. However, the role of environmental factors in CVDs is less known, and differences in available records were observed [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While most authors describe an inverse effect [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e10\u003c/span\u003e],, others suggested a direct association with mortality and morbidity during high and low ambient temperatures, atmospheric pressure, and wind speed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The study carried out in Scotland showed that the mortality trend had a steeper increase at lower temperatures than at warmer temperatures [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, another study reported lower numbers of CVDs hospitalizations during the warm season [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, different studies also reported that decreasing temperatures led to increasing vasopressin levels [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the outcomes of time series studies showed a minimum of 24 hours between the decrease in temperature and an increase in acute coronary syndrome (ACS) mortality [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Another study reported that CVDs mortality associated with temperature in the elderly subgroup was stronger than in younger age groups [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Even though most previous studies have considered only temperature as a meteorological factor, it seems wind speed factor may be a better predictor of mortality than temperature alone [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In Iran, there are different studies on the associated air pollution with CVDs but there are few studies associating the atmospheric factors (pressure, humidity, wind, and sunlight) with CVDs particularly in arid regions such as Gonabad city. To our knowledge, there is no study investigating the role of environmental stress in Gonabad. Thus, this is the first study that investigates the association between environmental factors and CVDs mortality in Gonabad city.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eGonabad city has been located in the south of Razavi Khorasan province with an area of nearly 105801 square km and 265 km away from Mashhad (the center of Razavi Khorasan province). The city has been located in an arid and semi-arid zone on the edge of the Lut desert (Figure.1). According to Gonabad synoptic stations, the prevailing wind direction is from the southeast (SE) to the northeast (NE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003efor this study was obtained from Gonabad University of Medical Sciences (IR.GMU.REC.1400.197). Additionally, \u003cb\u003et\u003c/b\u003eotal CVDs data were collected between 1st April 2017 and 31st December 2022 from Bohlol Gonabadi Hospital of Gonabad city. In addition, the meteorology data (temperature, humidity, wind speed, and pressure) on daily maximum and minimum were obtained from Gonabad Bureau Meteorology.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFirstly, mean monthly, seasonal, and annual CVDs mortality-hospitalization were calculated. Next, Meteorological data and CVDs mortality-hospitalization were matched with a time shift. Finally, the association between meteorological variability with CVDs mortality-hospitalization rate was investigated. Spring data was used as a reference to compare other seasons. Data were analyzed via Pearson correlation coefficients and negative binomial regression analysis. The effect of metrological factors on the cardiovascular hospitalization and mortality was first studied by Pearson regression for dependent count data. However, due to over-dispersion and lack of fitness with Pearson regression (according to statistical tests), negative binomial regression was used instead. Data were analyzed by using Stata software (version 17).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptive statistics of meteorological and CVDs demographics characteristics (mean, standard deviation, minimum, and maximum) for each season during the studied period and weather conditions. According to statistical analyses, the Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD was 61.52\u0026zwnj;\u0026plusmn;20.13 years old. However, 49.4% of the subjects were female. In addition, the mean age mortality and hospitalization were 76.8 and 60.7 years old, respectively. The mean weather temperature was 10.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13\u0026deg;C and 25.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58\u0026deg;C in cold and warm seasons, respectively, with intraday variations of up to 15\u0026deg;C. The cold season (lasts from October to March) and the warm season (runs from April to September) were classified. Morever, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows no association between gender and CVDs mortality, while the strongest association is observed between age and CVDs mortality (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.001). According to the obtained data CVDs, mortality increases by increasing each population year old [OR; 1.06 95% CI 1.05 to 1.07].\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\u003eClimate and characteristics of CVDs according Logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRecovery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eDemographics (n\u0026thinsp;=\u0026thinsp;6529)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e60.78\u0026thinsp;\u0026plusmn;\u0026thinsp;20.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.85\u0026thinsp;\u0026plusmn;\u0026thinsp;15.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.52\u0026zwnj;\u0026plusmn;20.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale ( N, % )\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3139 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3299 (49.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale ( N, % )\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3218 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3356 (50.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeteorological exposures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAverage, Temperature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCold season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWarm season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAverage, Humidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCold season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.43\u0026thinsp;\u0026plusmn;\u0026thinsp;18.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.98\u0026thinsp;\u0026plusmn;\u0026thinsp;18.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.19\u0026thinsp;\u0026plusmn;\u0026thinsp;18.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWarm season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.60\u0026thinsp;\u0026plusmn;\u0026thinsp;15.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.69\u0026thinsp;\u0026plusmn;\u0026thinsp;17.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.61\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAverage pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCold season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e896.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e896.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e896.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWarm season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e890.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e890.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e890.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAverage wind\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCold season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWarm season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between CVDs hospitalization and meteorological factors\u003c/h2\u003e \u003cp\u003eThe association between ambient temperature, relative air humidity, atmospheric pressure, and wind speed with the probability of CVDs hospitalization occurrence is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As can be seen, the incidence of emergency CVDs hospitalizations was significantly associated with humidity during the autumn season (P\u0026thinsp;=\u0026thinsp;0.01). Although, the temperature was also significantly associated with CVDs in the summer season (P\u0026thinsp;=\u0026thinsp;0.01). Additionally, Pearson correlations between hospitalization and wind speed factor showed positive correlations (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.05) in the summer and autumn seasons. Moreover, hospitalization varied seasonally with the peak number of hospitalization in winter followed by autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The cumulative percentiles of hospitalization counts are illustrated in Fig.\u0026nbsp;3. However, it was found that the peak number of daily hospitalizations was three persons (18%) followed by two persons (16%).\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\u003eAssociation CVDs hospitalization-mortality with meteorological factors according Pearson regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted coefficient (95% confidence interval)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003e\u003cb\u003eHospitalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHumidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.01 to 0.02]\u003c/p\u003e \u003cp\u003e[-0.07 to 0.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.02 to 0.02 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.03 to 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.1 to 0.1 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eWind\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.26 to 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ 0.10 to 0.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.17 to 0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.09 to 0.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eTemperature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.10 to 0.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.12 to 0.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.05 to 0.14]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.1 to 0.06 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.18 to 0.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.18 to 0.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -2.08 to -1.62]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.01 to 0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHumidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.18 to 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.00 to 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.00 to 0.01 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.01 to 0.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eWind\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.06 to 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.05 to 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.05 to 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.09 to 0.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eTemperature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.00 to 0.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.01 to 0.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.03 to 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[ -0.01 to 0.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.02 to 0.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.03 to 0.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.05 to 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFiqure 3.\u003c/b\u003e Cumulative percentiles of hospitalization counts were determined\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between CVDs mortality and meteorological factors\u003c/h2\u003e \u003cp\u003eThe association between ambient temperature, air humidity, atmospheric pressure, wind speed, and CVDs mortality occurrence probability is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The data show a positive association between CVDs mortality and wind factor in the warm season more than in the cold season. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the overall trend of CVDs mortality during various seasons over time. The cumulative trend of the data illustrated that the autumn season experienced a higher level of daily mortality in comparison with other seasons. Irrespective of that, the summer season showed the highest daily no-mortality counts. Moreover, according to the obtained data, nearly 87% of the days were recorded as non-mortality days during the studied period (Figure. 5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study presents that cardiovascular hospitalization was strongly linked to environmental factors and seasonal change. According to the World Health Organization Countdown, negative climate conditions have serious implications for public health including cardiac health (19). This study illustrated a seasonal variable hospitalization by using more than \u0026gt;\u0026thinsp;6529. The data showed an increase in daily CVDs hospitalization's number in the cold season. The highest number (4 people) of daily CVDs hospitalization (4.86%) strongly coincided with the winter followed by the autumn seasons. While the lowest number occurred in the spring season. It is undeniable that seasonal changes lead to behavioral pattern variations such as changes in diet, physical activity, and psychosocial factors that are considered key factors in high CVDs incidence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, it was suggested that cold-induced systemic hypertension and pulmonary hypertension were some of the most important risk factors that are leads to the incidence of CVDs hospitalizations in the cold season [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Previous evidence declared that luminal diameter significantly decreased after 1 min of cold-pressor stimulation. This may be why a lower temperature range is the main environmental factor leading to higher incidences of hospitalization in cardiology wards [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. According to the findings of this study, there exists a significant association between lower humidity and CVDs hospitalization (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.013 [Coefficient\u0026thinsp;\u0026minus;\u0026thinsp;0.00 [-0.00 to 0.01]) in the autumn season. However, cold and low humidity led to an increase in CVDs hospitalization incidences. The results obtained by Lee et al. support our results [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, Panagiotakos et al. reported a positive correlation between humidity and hospital admission [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Swartz et al. mentioned that there was no evidence of CVDs hospitalization and humidity effect [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Although there is no clear evidence of the exact humidity mechanism on the CVDs accuracy, some scientists believe that high humidity weather interferes with the processes of perspiration and body temperature homeostasis, resulting in respiratory fatigue and increased heart rate [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. From this point of view, very low humidity and low temperatures can intensify the effect of each other and increase the incidence of respiratory diseases and influenza, which, in turn, can increase CVDs incidence [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, hospitalization outcomes risk in both cold (autumn) and warm (summer) season was associated with wind speed positively (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.05 (Coefficient, 0.03 [-0.06 to 0.00]) and negatively (Coefficient, \u0026minus;\u0026thinsp;0.02 [-0.05 to 0.00], respectively. The inverse relationship between wind speed and CVDs hospitalization has been well-documented in previous literature [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR28\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this case, it seems that complex topography and strong tropical seasonal wind in Kavir and Lut deserts are directly responsible for the transport of pollutants in the large-scale area. Interestingly, in the summer higher temperatures and air inversion can intensify the increasing healthy effect of tropical wind and, therefore, hospitalization [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on our results, the number of CVDs hospitalizations incidents was strongly associated with higher ambient temperature (coefficient, 0.036 [-0.00 to 0.07] per ℃, P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.016) in the summer season. The role of environmental temperature is conflicting so the approach is debatable. The effect of hot temperatures on emergency hospital admissions for CVDs was reported widely in different climate situation regions [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR31\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Harvesting and agricultural activities intensify the effect of hot temperatures in arid and semi-arid regions. This phenomenon causes stressor effects, mainly on a pool of frail individuals, which leads to more heart attacks and CVDs hospitalization [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, the scientific explanation of the effect of temperature on the increasing CVDs hospitalization is that heat damages the structure of endothelial cells in coronary arteries and increases the permeability of the tunica intima. Reduces the superoxide dismutase (SOD) activity of heart tissues and finally increases the lipoproteins in the oxygenated blood [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. As a result, a large amount of cholesterol settled on the tunica intima causing atherosclerosis and exacerbating coronary heart disease. However, our data showed a positive correlation between wind speed and CVDs mortality (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.057) in summer. Nevertheless, CVDs mortality increased with decreasing wind speed [Coefficient, -0. 13 (\u0026minus;\u0026thinsp;0.26 to 0.00)].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe arid and semi-arid region's population suffers from various environmental problems in Iran that are rooted in tough weather conditions. To the best of our knowledge, this study represents the first attempt to do so in an arid city with variable and specific weather conditions. The scenario under analysis estimated that the mean monthly hospitalization in the cold seasons is higher than in the hot seasons. In addition, according to the obtained data, CVDs hospitalization has a significant positive association with wind and temperature factors in the hot season while a negative association with humidity and wind in the cold season was observed. Similarly, it shows a negative association between CVDs mortality and wind speed in summer. Overall, this study strongly suggests taking action against sudden climate phenomena in arid and semi-arid regions (with complex weather) for the protection of the vulnerable population and prevention of CVDs incidents\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowlagements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to appreciate the Social determining Health Research Center Gonabad university of medical science for supporting.\u003c/p\u003e\n\u003cp\u003efor their helpful comments on the manuscript\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eLaleh R. Kalankesh, Ali Mohammadpour, and Ali Alami designed the stud\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eZahed rezaei and Arash Prvari analyzed and modeled the data.\u003c/p\u003e\n\u003cp\u003eLaleh R.Kalankesh was writing the paper.\u003c/p\u003e\n\u003cp\u003eSusana Rodriguez-Couto and shahla khosravan edit and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that all methods were carried out in accordance with relevant guidelines and regulations. Ethics committee of Gonabad university of Medical science waived the need for informed consent based on the study\u0026rsquo;s retrospective analysis of patient data. All experimental protocols of this study were approved by the ethics committee of Gonabad university of Medical science, Iran (IR.GMU.REC.1400.197).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Social Determinants of Health Research Center, Gonabad University of Medical sciences, Gonabad, Iran\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eFaculty of Health, Tehran University of Medical Science, Tehran, Iran\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Department of Separation Science, LUT School of Engineering Science, LUT University, Sammonkatu 12, 50130 Mikkeli, Finland\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eParker, E.R., The influence of climate change on skin cancer incidence \u0026ndash; A review of the evidence. International Journal of Women\u0026apos;s Dermatology, 2021. 7; 1. 17-27.\u003c/li\u003e\n\u003cli\u003eGrigorieva, E.A. and B.A. Revich, Health Risks to the Russian Population from Temperature Extremes at the Beginning of the XXI Century. Atmosphere, 2021. 12; 10. 1331.\u003c/li\u003e\n\u003cli\u003eTian, G., Z. Qiao, and X. Xu, Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001\u0026ndash;2012 in Beijing. Environmental pollution, 2014. 192. 266-274.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Amato, G., et al., Meteorological conditions, climate change, new emerging factors, and asthma and related allergic disorders. A statement of the World Allergy Organization. World Allergy Organ J, 2015. 8; 1. 25-25.\u003c/li\u003e\n\u003cli\u003eMousavi, A., et al., Climate change and health in Iran: a narrative review. J Environ Health Sci Eng, 2020. 18; 1. 367-378.\u003c/li\u003e\n\u003cli\u003eA\u0026apos;yun, I.Q. and U. Khasanah, The Impact of Economic Growth and Trade Openness on Environmental Degradation: Evidence from A Panel of ASEAN Countries. Jurnal Ekonomi \u0026amp; Studi Pembangunan, 2022. 23; 1. 81-92.\u003c/li\u003e\n\u003cli\u003eSarrafzadegan, N. and N. Mohammadifard, Cardiovascular disease in Iran in the last 40 years: prevalence, mortality, morbidity, challenges and strategies for cardiovascular prevention. Arch Iran Med 2019. 22; 4. 204-210.\u003c/li\u003e\n\u003cli\u003eBemanalizadeh, M., Z. Farajzadegan, and P. Golshiri, Estimation of Cardiovascular Disease Risk Factors in the Undefined Participants of Campaign in Isfahan in 2017. Int J Prev Med, 2021. 12. 47-47.\u003c/li\u003e\n\u003cli\u003eDadbakhsh, M., N. Khanjani, and A. Bahrampour, The relation between mortality from cardiovascular diseases and temperature in Shiraz, Iran, 2006-2012. ARYA Atheroscler, 2018. 14; 4. 149-156.\u003c/li\u003e\n\u003cli\u003eMoghadamnia, M.T., et al., The effects of apparent temperature on cardiovascular mortality using a distributed lag nonlinear model analysis: 2005 to 2014. Asia Pacific Journal of Public Health, 2018. 30; 4. 361-368.\u003c/li\u003e\n\u003cli\u003eMoghadamnia, M.T., et al., Ambient temperature and cardiovascular mortality: a systematic review and meta-analysis. PeerJ, 2017. 5. e3574-e3574.\u003c/li\u003e\n\u003cli\u003eHuang, H., et al., Spatio-temporal mechanism underlying the effect of urban heat island on cardiovascular diseases. Iranian journal of public health, 2020. 49; 8. 1455.\u003c/li\u003e\n\u003cli\u003eCarder, M., et al., The lagged effect of cold temperature and wind chill on cardiorespiratory mortality in Scotland. Occupational and environmental medicine, 2005. 62; 10. 702-710.\u003c/li\u003e\n\u003cli\u003eBoussoussou, N., et al., Complex effects of atmospheric parameters on acute cardiovascular diseases and major cardiovascular risk factors: data from the Cardiometeorology(SM) study. Sci Rep, 2019. 9; 1. 6358-6358.\u003c/li\u003e\n\u003cli\u003eCampbell, G.A. and M.H. Rosner, The agony of ecstasy: MDMA (3, 4-methylenedioxymethamphetamine) and the kidney. Clinical Journal of the American Society of Nephrology, 2008. 3; 6. 1852-1860.\u003c/li\u003e\n\u003cli\u003eLi, Y., et al., Association between high temperature and mortality in metropolitan areas of four cities in various climatic zones in China: a time-series study. Environmental Health, 2014. 13; 1. 1-10.\u003c/li\u003e\n\u003cli\u003eRockl\u0026ouml;v, J., et al., Susceptibility to mortality related to temperature and heat and cold wave duration in the population of Stockholm County, Sweden. Global health action, 2014. 7; 1. 22737.\u003c/li\u003e\n\u003cli\u003eMohan, M., A. Gupta, and S. Bhati, A modified approach to analyze thermal comfort classification. Atmospheric and Climate Sciences, 2014. 4; 1. 7-19.\u003c/li\u003e\n\u003cli\u003eDennison, R.A., et al., The association between psychosocial factors and change in lifestyle behaviour following lifestyle advice and information about cardiovascular disease risk. BMC Public Health, 2018. 18; 1. 731-731.\u003c/li\u003e\n\u003cli\u003eFares, A., Winter cardiovascular diseases phenomenon. N Am J Med Sci, 2013. 5; 4. 266-279.\u003c/li\u003e\n\u003cli\u003eYoneyama, K., et al., Weather temperature and the incidence of hospitalization for cardiovascular diseases in an aging society. Sci Rep, 2021. 11; 1. 1-11.\u003c/li\u003e\n\u003cli\u003eLee, J.H., et al., Influence of weather on daily hospital admissions for acute myocardial infarction (from the Korea Acute Myocardial Infarction Registry). International journal of cardiology, 2010. 144; 1. 16-21.\u003c/li\u003e\n\u003cli\u003ePanagiotakos, D.B., et al., Climatological variations in daily hospital admissions for acute coronary syndromes. International journal of cardiology, 2004. 94; 2-3. 229-233.\u003c/li\u003e\n\u003cli\u003eSchwartz, J., J.M. Samet, and J.A. Patz, Hospital admissions for heart disease: the effects of temperature and humidity. Epidemiology, 2004. 15; 6. 755-761.\u003c/li\u003e\n\u003cli\u003eHiguma, T., et al., Effects of temperature and humidity on acute myocardial infarction hospitalization in a super-aging society. Sci Rep, 2021. 11; 1. 1-10.\u003c/li\u003e\n\u003cli\u003eZhang, R., et al., The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Respiratory Research, 2021. 22; 1. 153.\u003c/li\u003e\n\u003cli\u003eQiu, X., et al., Inverse probability weighted distributed lag effects of short-term exposure to PM2. 5 and ozone on CVD hospitalizations in New England Medicare participants-Exploring the causal effects. Environmental research, 2020. 182. 109095.\u003c/li\u003e\n\u003cli\u003eRadi\u0026scaron;auskas, R., et al., Trends of myocardial infarction morbidity and its associations with weather conditions. Medicina, 2014. 50; 3. 182-189.\u003c/li\u003e\n\u003cli\u003eOgbomo, A.S., et al., Vulnerability to extreme-heat-associated hospitalization in three counties in Michigan, USA, 2000\u0026ndash;2009. International journal of biometeorology, 2017. 61; 5. 833-843.\u003c/li\u003e\n\u003cli\u003eGiang, P.N., et al., The effect of temperature on cardiovascular disease hospital admissions among elderly people in Thai Nguyen Province, Vietnam. Global health action, 2014. 7. 23649-23649.\u003c/li\u003e\n\u003cli\u003eLi, M., et al., Impact of Extremely Hot Days on Emergency Department Visits for Cardiovascular Disease among Older Adults in New York State. Int J Environ Res Public Health, 2019. 16; 12. 2119.\u003c/li\u003e\n\u003cli\u003eZhai, G., et al., The effect of apparent temperature on hospital admissions for cardiovascular diseases in rural areas of Pingliang, China. Annals of Agricultural and Environmental Medicine, 2022. 29; 2. 281\u0026ndash;286.\u003c/li\u003e\n\u003cli\u003eBell, J.E., et al., \u003cem\u003eCh. 4: Impacts of extreme events on human health\u003c/em\u003e, 2016, US Global Change Research Program, Washington, DC. p. 99-128.\u003c/li\u003e\n\u003cli\u003eFavero, G., et al., Endothelium and its alterations in cardiovascular diseases: life style intervention. BioMed research international, 2014. 2014. 1-23.\u003c/li\u003e\n\u003c/ol\u003e\n"}],"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":"Iran, Environmental, Cardiovascular, hospitalization, mortality","lastPublishedDoi":"10.21203/rs.3.rs-4086774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4086774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSince questioning regarding climate and incidents of cardiovascular rates have been debated. This study was designed to investigate the association between cardiovascular mortality hospitalization risk and demographic-meteorological factors in the arid and semi-arid zone of Iran during (1st April 2017 and 31st December 2022).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eLogistic and negative binomial regression and Pearson regression were used for analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA mean age of 61.52 years old (49.5% female and 50.5% male) was recorded for the hospitalization. Peak numbers of daily hospitalization were observed in winter (18%), followed by autumn (16%). Cardiovascular hospitalization presented a significant positive correlation with the wind (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.05) and temperature (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.016) in the hot season, whereas showing a negative correlation with humidity (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.013) and wind factor (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.05) in the cold season. Similarly, a negative correlation between cardiovascular mortality and speed of wind (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.05) was observed in summer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIt concluded that there were associations between demographic-meteorological factors and the occurrence of cardiovascular hospitalization-mortality in arid and semiarid region.\u003c/p\u003e","manuscriptTitle":"Socio environmental determinants of cardiovascular mortality and hospitalization risk in arid and semi-arid regions – a case study for Gonabad city,Iran 2017-2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 17:46:47","doi":"10.21203/rs.3.rs-4086774/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":"ad73189d-20e0-4287-adb7-63f70169866b","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T09:45:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 17:46:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4086774","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4086774","identity":"rs-4086774","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}