https://orcid.org/0000-0001-6818-2347
Figures
Abstract
Chronic rhinosinusitis (CRS) is a prevalent and disabling paranasal sinus disease associated with some environmental factors. In this study, we evaluated the effect of geo-climatic factors on CRS in a region of southwest Iran. The study mapped the residency addresses of 232 patients with CRS who lived in Kohgiluyeh and Boyer-Ahmad province and had undergone sinus surgery from 2014 to 2019. The effects of Mean Annual Humidity (MAH), Mean Annual Rainfall (MAR), Mean Annual Temperature (MAT), maximum MAT (maxMAT), minimum MAT (minMAT), Mean Annual Evaporation (MAE), wind, elevation, slope, and land cover were assessed on the occurrence of CRS using Geographical Information System (GIS). Statistical analysis was performed using univariate and multivariate binary logistic regression. Patients came from 55 points including villages, towns, and cities. In univariate analysis, climatic factors including MAT (OR = 0.537), minMAT (OR = 0.764), maxMAT (OR = 0.63), MAR (OR = 0.994), and MAH (OR = 0.626) were significantly related to CRS occurrence. Elevation (OR = 0.999), slope (OR = 0.9), and urban setting (OR = 24.667) were the significant determinants among geographical factors when analyzed independently. The multivariate analysis found maxMAT (OR = 0.5), MAR (OR = 0.994), elevation (OR = 0.998), and urban (OR = 16.8) as significant factors affecting CRS occurrence. The urban setting is the most critical factor affecting CRS disease. Cold and dry areas and low attitude are the other risk factors for CRS in Kohgiluyeh and Boyer-Ahmad province, southwest Iran.
Citation: Ghatee MA, Kanannejad Z, Nikaein K, Fallah N, Sabz G (2023) Geo-climatic risk factors for chronic rhinosinusitis in southwest Iran. PLoS ONE 18(7): e0288101. https://doi.org/10.1371/journal.pone.0288101
Editor: Rajeev Singh, Satyawati College, University of Delhi, INDIA
Received: November 8, 2022; Accepted: June 20, 2023; Published: July 5, 2023
Copyright: © 2023 Ghatee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data is available upon request due to ethical considerations. The contact address and contact information for data request is: Yasuj University of Medical Sciences (YUMS) Research and Technology Vice Chancellor office, Shahid Motahari Blvd, Yasuj, Iran 0098-74-33346078 0098- 74-33346079 research@yums.ac.ir.
Funding: This study was financially supported by Yasuj University of Medical Sciences (Grant No. 960627). Grant was received by Dr. Gholamabbas Sabz. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Chronic rhinosinusitis (CRS) is inflammation of the nasal cavity and paranasal sinuses, often classified based on the duration of symptoms and inflammation: less than one month (acute), between one and three months (sub-acute), and more than three months (chronic) [1]. It is one of the most debilitating diseases, with a prevalence of around 6–15% for acute and 12% for CRS worldwide [2]. CRS management needs frequent outpatient visits and a notable diagnostic and therapeutic measure, which leads to reduced productivity in health services [3].
Climatic change and air pollution are global issues that are largely caused by human activities such as burning fossil fuels, deforestation, and industrialization. It has serious health impacts, as it can cause respiratory tract issues like rhinosinusitis, asthma, and chronic obstructive pulmonary disease (COPD), which can be exacerbated by changes in temperature and humidity [4]. There is a significant association between occupational and environmental risk factors and CRS [5, 6]. Occupational exposure to dust, poisonous gas, noxious inhalant compounds, and environmental factors such as woodstove, indoor tobacco smoke, air pollution, pets, or carpet at home are risk factors for CRS [7, 8]. Airborne diseases, including those caused by bacteria, viruses, and fungi, can also be more easily spread in areas with high levels of air pollution, which can make them particularly problematic in urban areas where air quality is poor and affects CRS [9, 10]. Also, limited ecological studies have been conducted and showed humidity, temperature, and wind speed are climatic risk factors for CRS [7].
Geographical Information System (GIS) has become an essential tool in medical epidemiological studies, monitoring, and control of disease and can be used for public health planning and disease risk forecasting. By GIS, the danger zones of disease are determined, and the relationship between the geographical features and the disease occurrence is examined by preparing thematic graphic maps. Therefore, this technology can facilitate determining the occurrence and distribution of disease and controlling and managing it as quickly as possible [11]. GIS has become an important tool in determining the geo-climatic factors and risk zones for a variety of diseases, including both infectious and non-infectious diseases [12–19].
Finding risk zones of CRS using GIS technology can help researchers reduce the disease’s progression in society and decrease the related complications and costs. There is little information about the effect of environmental factors on CRS by GIS-based approaches. Two studies used this method for ambient exposure characterization, generally by calculating the distance of residence to the source of environmental pollution, such as intensive hog farming, industries, and dust-producing activities [20, 21].
To our knowledge, this is the first comprehensive GIS-based study investigating the relationship between geo-climatic factors and CRS worldwide. The use of GIS can help to identify areas where the environmental conditions are favorable for the development of CRS, as well as areas where the disease is more prevalent. By analyzing factors such as temperature, humidity, evaporation, rainfall, wind speed, elevation, slope, and land cover, we can gain insights into the underlying environmental drivers of the CRS in region of Kohgiluyeh and Boyer-Ahmad province, southwest Iran.
Materials and methods
Study area
The study areas included Boyer-Ahmad and Dena counties in Kohgiluyeh and Boyer-Ahmad province with latitudes of 30°9’ and 31°32’N and longitudes of 49°57’and 50°42’, respectively, in southwest Iran. The capitals of Boyer-Ahmad and Dena are Yasuj and Sisakht, respectively. Boyer-Ahmad and Dena counties cover an area of about 65,000 km2 and 1,821 km2, respectively, and are located in a cold climatic region. These counties include areas in one of the highest altitudes in the central part of the Zagros mountain chain with high annual snow and rainfall. Dena Peak, the ninth highest peak in Iran, with a height of 4,409 meters, is located in this area. The study area has diverse vegetation with nearly 2,000 plant species.
Data collection
This five-year cross-sectional study included all CRS patients undergoing sinus surgery from 2014 to 2019 in Boyer-Ahmad and Dena counties. Patient data were collected from hospital records. The home addresses of 232 patients were entered in the attributes of the GIS layer of the province’s political divisions. The spatial point layer of the city and villages of the studied counties and the polygonal layer of counties were extracted from whole province layers using ArcMap software. The study was approved by the Ethics Committee of Yasuj University of Medical Sciences (IR.YUMS.REC.1398.143).
Geo-climatic data
The meteorological data in the study period were acquired from the Kohgiluyeh and Boyer-Ahmad Province Weather Bureau. The data included temperature, humidity, evaporation, and wind speed obtained from six synoptic meteorological stations and rainfall data from 44 rain-gauge stations distributed in the province.
Mean Annual Temperature (MAT), maximum MAT (maxMAT), minimum MAT (minMAT), Mean Annual Rainfall (MAR), Mean Annual Evaporation (MAE), Mean Annual Humidity (MAH), and mean annual wind speed were calculated. The iso-hydral and iso-humid raster layers were generated using the Kriging interpolation method, and the iso-thermal, iso-evaporation, and iso-wind speed layers using the tension-based Spline interpolation model with a resolution grid of 1 × 1 km.
The digital elevation model raster layer and land cover vector layer were retrieved from the Department of Natural Resources in Kohgiluyeh and Boyer-Ahmad province. The slope raster layer was generated based on the Digital Elevation Model (DEM) map by using the spatial analyst tool to calculate the maximum rate of change in the value between each cell and its neighbors.
Geospatial and statistical analysis
The point layers of cities and villages of the studied counties were extracted with the raster layers, and then the geometric intersections of the obtained layer and land cover vector layer were computed by the identity tool to generate the final layer. Each point represented geo-climatic values of all overlapped raster and vector layers in the final layer. The association between geo-climatic factors and CRS was assessed based on the spatial description of patients in Kohgiluyeh and Boyer-Ahmad province. Residential points data, including CRS reported and non-reported villages and cities, were extracted from the final province villages/cities’ point layers and analyzed using univariate and multivariate logistic regression models. The statistical analyses were performed using SPSS version 21.
Results
Geo-climatic distribution of points with CRS
Patients were reported from 55 points in the current study. CRS cases were reported from different areas, but most were from the east and north of the studied area (Figs 1–3).
MAT (A), minMAT (B), maxMAT (C) raster models. Points with rhinosinusitis were shown by triangle symbol. Mean annual temperature (MAT), maximum (max), minimum (min).
MAR (A), MAH (B), MAE (C), wind speed (D) raster models. Points with rhinosinusitis were shown by triangle symbol. Mean annual rainfall (MAR), mean annual humidity (MAH), mean annual evaporation (MAE).
Maps of the geographical factors: DEM (A), slope (B), and land cover maps (C). Points with CRS were shown by triangle symbol. DEM (digital elevation model).
Univariate logistic regression
As shown in Table 1, among all climatic factors, MAT, minMAT, maxMAT, MAR, and MAH were detected as significant factors associated with CRS. Each degree increase in MAT (CI = 0.404–0.714, OR = 0.537), minMAT (CI = 0.651–0.895, OR = 0.764), and maxMAT (CI = 0.518–0.764, OR = 0.630) reduced the chance of disease by 46.3%, 23.6%, and 37%, respectively. Besides, MAR (CI = 0.988–0.999, OR = 0.994) and MAH (CI = 0.498–0.788, OR = 0.626) had inverse effects on CRS occurrence. Accordingly, each one-milliliter increase in rainfall and one percentage increase in humidity reduced the chance of disease by 1.6% and 38.4%, respectively. However, MAE and wind speed had no significant effect on the occurrence of CRS.
Table 2 shows the effect of geographical parameters on CRS. Urban setting, DEM, and slope were the environmental determinants of CRS. Urban setting increased the odds of CRS by 24.6 folds (CI = 4.534–134.190, OR = 24.667), while DEM (CI = 0.998–0.999, OR = 0.999) and slope (CI = 0.855–0.952, OR = 0.902) decreased the disease occurrence by 0.1% and 9.8%, respectively. Also, there was an inverse trend for semi-condensed forest areas, but it was not significant.
Multivariate logistic regression model
In the multivariate analysis, all significant factors in the univariate regression model were computed by the forward stepwise method of multivariate regression analysis. In this method, only land covers, maxMAT, MAR, and DEM were entered in the last computation step by the model, which appeared as significant variables affecting CRS. As shown in Table 3, among these parameters, the urban setting (CI = 2.696–105.707, OR = 16.88) was the only factor associated with increased CRS occurrence, while there was a decreasing trend for DEM (CI = 0.669–0.999, OR = 0.998), maxMAT (CI = 0.371–0.679, OR = 0.502), and MAR (CI = 0.982–1.020, OR = 0.994). Other geo-climatic factors were not detected as significant variables.
Discussion
This study showed that the urban setting, maxMAT, MAR, and DEM had the most effects on CRS in sequence. Besides, MAT, MAH, minMAT, and slope were determining factors when their effects were analyzed independently.
Geographical factors
Based on the results, among various types of land cover, the urban setting was the most important factor directly affecting CRS occurrence. Our results are consistent with some studies showing a higher prevalence of allergic rhinitis and nasal symptoms among people born and raised in urban areas than among those in rural areas [22–25]. The microbial load and diversity accompanied by farm living in rural areas have been suggested as a reason for the beneficial effects on respiratory diseases [26]. However, some studies failed to identify this protective effect and reported that farming activities and poor hygiene were associated with increased prevalence of rhinitis in such areas [27, 28]. Another possible explanation of the direct association between urbanization and sinus problem is increased air pollution, dust particles, factories, sensitizing chemicals, and waste in urban areas [29–31]. A recent study by Zhang et al. reported long-term exposure to airborne particulate matter ≤2.5 in aerodynamic diameter (PM2.5) as a risk factor for developing CRS in non-allergic patients [32]. In addition, the increasing agglomeration of people in dense urban areas increased the risk of contagious diseases, especially rhino, influenza, and parainfluenza viruses, contributing to disease exacerbation [33–36].
As another influential geographical factor, elevation was inversely associated with CRS in the study area, where more CRS was reported among villages or cities with lower elevation. It may be due to high population density in regions with low elevation in mountainous areas associated with increased air pollutants, nanoparticles, and infectious viral diseases [36]. There was no prior study investigating the effect of altitude on CRS, while some reported the benefits of permanent high-altitude residence for reducing exacerbations [37] and long-term high-altitude residence (>12 weeks) for improving symptoms and lung function [38]. However, individuals whose asthmatic trigger is cold air at lower humidity may be more prone to asthma exacerbations at a high altitude, especially during exercise [39].
We also showed more CRS in areas with a lower slope for the first time. Human populations have more tendencies to areas with lower slopes for ease of movement. So, more populated areas and, accordingly, CRS predisposing factors can be found in lower slope regions.
Climatic factors
Based on the multivariate logistic study, maxMAT was the most important climatic factor that was inversely related to CRS occurrence. An inverse association also was found for humidity in univariate analysis. While high temperature and humidity are protective factors against CRS, cold and dry climates could worsen its condition. Inhalation of cold and dry air leads to the congestion of elastic tissue of the nose that reduces the heat exchange between the inhaled and exhaled air. Lack of moisture in the air can dry out the sinuses, leading to irritation and thickening mucus. Together, these factors impair mucociliary transport function and result in nasal obstruction, which can be a precursor to CRS [27, 28]. With extra time spent indoors during the colder weather, patients are more prone to indoor allergy attacks, which is one of the potential risk factors for CRS [5]. In addition, cold is the main trigger of viral sinus infection that blocks off the drainage channels of sinuses [40]. Our results contrast with some previous studies that reported a high incidence of CRS at higher temperatures [8]. They believed that the increased temperature and humidity create suitable conditions for fungi growth [8]. However, these studies investigated only fungal CRS, a sinus infection resulting from a fungus, while most surgical CRS cases in this study were allergic or associated with polyps (unpublished data). Furthermore, our study focused on cold mountainous areas where high temperatures can be more suitable for health maintenance.
We detected MAR as a protective factor against CRS by multivariate logistic regression. Rainfall events may eliminate dust particles, air pollutants, and other airborne particles, which are the environmental risk factors for CRS [41, 42]. However, some studies failed to detect a significant association between rain and CRS [41]. In contrast to our findings, increased rainfall was a risk factor for disease progression in patients with fungal rhinosinusitis [43]. In addition, increased MAR was associated with increased humidity, which is another significant factor in our study when analyzed independently. We showed that increased humidity is associated with decreased incidence of CRS. Sinusitis experts agree that adding humidity to the air with a humidifier is generally good for sinus health. Humidity can help nasal congestion by providing more moisture and humidity within the nose. Like rainfall, some studies reported humidity as a negative factor influencing fungal rhinosinusitis [44, 45].
Conclusions
In short, the urban setting was the most critical risk factor for CRS, and lower altitude and slope were the other geographical factors. This study demonstrated that cold and dry climatic conditions were major meteorological risk factors for CRS development in this region, while higher temperatures, rainfall, and humidity were protective factors. These findings can determine the high-risk areas of CRS in this region of southwest Iran that patients and health professionals should consider. Further research is needed to address the risk factors associated with urban settings like air pollution or socioeconomic factors in a large retrospective study.
References
- 1. Orlandi RR, Kingdom TT, Smith TL. International consensus statement on allergy and rhinology: rhinosinusitis 2021. Int Forum Allergy Rhinol. 2021;11(3):213–739. pmid:33236525
- 2. Shi JB, Fu QL, Zhang H, Cheng L, Wang YJ, Zhu DD, et al. Epidemiology of chronic rhinosinusitis: results from a cross-sectional survey in seven Chinese cities. Allergy. 2015;70(5):533–9. pmid:25631304
- 3. Hamilos DL. Chronic rhinosinusitis: epidemiology and medical management. J Allergy Clin Immunol. 2011;128(4):693–707; quiz 8–9. pmid:21890184
- 4. Mishra MM, Sahu N, Mallick G, Pani B. Knowledge, Perception, and Behaviour Concerning Impact of Climate Variability on Health: A Cross-sectional Study in the Tribal-dominated Kalahandi District of Odisha, India. J Commun Dis. 2022;54(2):39–48.
- 5. Gao WX, Ou CQ, Fang SB, Sun YQ, Zhang H, Cheng L, et al. Occupational and environmental risk factors for chronic rhinosinusitis in China: a multicentre cross-sectional study. Respir Res. 2016;17(1):54. pmid:27184027
- 6. Sundaresan AS, Hirsch AG, Storm M, Tan BK, Kennedy TL, Greene JS, et al. Occupational and environmental risk factors for chronic rhinosinusitis: a systematic review. Int Forum Allergy Rhinol. 2015;5(11):996–1003. pmid:26077513
- 7. Dietz de Loos DAE, Ronsmans S, Cornet ME, Hellings PW, Hox V, Fokkens WJ, et al. Occupational exposure influences control of disease in patients with chronic rhinosinusitis. Rhinology. 2021;59(4):380–6. pmid:34282809
- 8. AlQahtani A, Alim B, Almudhaibery F, Mulafikh D, Almutairi S, Almohanna S, et al. The Impact of Climatic, Socioeconomic, and Geographic Factors on the Prevalence of Allergic Fungal Rhinosinusitis: A Worldwide Ecological Study. Am J Rhinol Allergy. 2022:19458924211069226. pmid:35187957
- 9. Tan KS, Yan Y, Ong HH, Chow VT, Shi L, Wang DY. Impact of respiratory virus infections in exacerbation of acute and chronic rhinosinusitis. Curr Allergy Rep. 2017;17:1–8. pmid:28389843
- 10. Walters ZA, Sedaghat AR, Phillips KM. Acute exacerbations of chronic rhinosinusitis: The current state of knowledge. Laryngoscope Investig Otolaryngol. 2022;7(4):935–42. pmid:36000029
- 11. Scholten HJ, de Lepper MJ. The benefits of the application of geographical information systems in public and environmental health. World Health Stat Q. 1991;44(3):160–70. pmid:1949884
- 12. Kanannejad Z, Haghdoost AL, Ghatee MA, Azarifar FA, Shahriari SA, Moshfe AB. Effect of human, livestock population, climatic and environmental factors on the distribution of brucellosis in southwest Iran. Acta Med Mediterr. 2019 Jan 1;35(4):2259–64.
- 13. Samany NN, Liu H, Aghataher R, Bayat M. Ten GIS-Based Solutions for Managing and Controlling COVID-19 Pandemic Outbreak. SN Comput Sci. 2022 May 5;3(4):269. pmid:35531569
- 14. Ghatee MA, Fakhar M, Derakhshani‐Niya M, Behrouzi Z, Hosseini Teshnizi S. Geo‐climatic factors in a newly emerging focus of zoonotic visceral leishmaniasis in rural areas of north‐eastern Iran. Transbound Emerg Dis. 2020 Mar;67(2):914–23. pmid:31698533
- 15. Ghatee MA, Nikaein K, Taylor WR, Karamian M, Alidadi H, Kanannejad Z, et al. Environmental, climatic and host population risk factors of human cystic echinococcosis in southwest of Iran. BMC Public Health. 2020 Dec;20(1):1–3.
- 16. Vladimirov LN, Machakhtyrov GN, Machakhtyrova VA, Louw AS, Sahu N, Yunus AP, et al. Quantifying the northward spread of ticks (Ixodida) as climate warms in Northern Russia. Atmosphere. 2021 Feb 8;12(2):233.
- 17. Koye DN, Melaku YA, Gelaw YA, Zeleke BM, Adane AA, Tegegn HG, et al. Mapping national, regional and local prevalence of hypertension and diabetes in Ethiopia using geospatial analysis. BMJ open. 2022 Dec 1;12(12):e065318. pmid:36600383
- 18. Brousmiche D, Lanier C, Cuny D, Frevent C, Genin M, Blanc-Garin C, et al. How do territorial characteristics affect spatial inequalities in the risk of coronary heart disease?. Sci Total Environ. 2023 Jan 12:161563. pmid:36640871
- 19. Kanannejad Z, Shomali M, Esmaeilzadeh H, Nabavizadeh H, Nikaein K, Ghahramani Z, et al. Geoclimatic risk factors for childhood asthma hospitalization in southwest of Iran. Pediatr Pulmonol. 2022 Sep;57(9):2023–31. pmid:35560812
- 20. Villeneuve PJ, Ali A, Challacombe L, Hebert S. Intensive hog farming operations and self-reported health among nearby rural residents in Ottawa, Canada. BMC public health. 2009;9:330. pmid:19744310
- 21. Alexiou A, Sourtzi P, Dimakopoulou K, Manolis E, Velonakis E. Nasal polyps: heredity, allergies, and environmental and occupational exposure. J Otolaryngol Head Neck Surg. 2011;40(1):58–63. pmid:21303603
- 22. Riedler J, Eder W, Oberfeld G, Schreuer M. Austrian children living on a farm have less hay fever, asthma and allergic sensitization. Clin Exp Allergy 2000;30(2):194–200. pmid:10651771
- 23. Von Ehrenstein OS, Von Mutius E, Illi S, Baumann L, Böhm O, von Kries R. Reduced risk of hay fever and asthma among children of farmers. Clin Exp Allergy. 2000;30(2):187–93. pmid:10651770
- 24. Chu LM, Rennie DC, Cockcroft DW, Pahwa P, Dosman J, Hagel L, et al. Prevalence and determinants of atopy and allergic diseases among school-age children in rural Saskatchewan, Canada. Ann Allergy Asthma Immunol. 2014;113(4):430–9. pmid:25129487
- 25. Kajiwara-Morita A, Karunanayake CP, Dosman JA, Lawson JA, Kirychuk S, Rennie DC, et al. Prevalence and Determinants of Sinus Problems in Farm and Non-Farm Populations of Rural Saskatchewan, Canada. Sinusitis. 2018;3(1):2.
- 26. Ege MJ, Mayer M, Normand AC, Genuneit J, Cookson WO, Braun-Fahrländer C, et al. Exposure to environmental microorganisms and childhood asthma. N Engl J Med. 2011;364(8):701–9. pmid:21345099
- 27. Brunekreef B, Von Mutius E, Wong GK, Odhiambo JA, Clayton TO. Early life exposure to farm animals and symptoms of asthma, rhinoconjunctivitis and eczema: an ISAAC Phase Three Study. Int J Epidemiol. 2012;41(3):753–61. pmid:22287135
- 28. Cooper PJ, Vaca M, Rodriguez A, Chico ME, Santos DN, Rodrigues LC, et al. Hygiene, atopy and wheeze-eczema-rhinitis symptoms in schoolchildren from urban and rural Ecuador. Thorax. 2014;69(3):232–9. pmid:24105783
- 29. Baumann LM, Romero KM, Robinson CL, Hansel NN, Gilman RH, Hamilton RG, et al. Prevalence and risk factors for allergic rhinitis in two resource-limited settings in Peru with disparate degrees of urbanization. Clin Exp Allergy. 2015;45(1):192–9. pmid:25059756
- 30. Nicolaou N, Siddique N, Custovic A. Allergic disease in urban and rural populations: increasing prevalence with increasing urbanization. Allergy. 2005;60(11):1357–60. pmid:16197466
- 31. Bowatte G, Lodge C, Lowe AJ, Erbas B, Perret J, Abramson MJ, et al. The influence of childhood traffic-related air pollution exposure on asthma, allergy and sensitization: a systematic review and a meta-analysis of birth cohort studies. Allergy. 2015;70(3):245–56. pmid:25495759
- 32. Zhang Z, Kamil RJ, London NR, Lee SE, Sidhaye VK, Biswal S, et al. Long-Term Exposure to Particulate Matter Air Pollution and Chronic Rhinosinusitis in Nonallergic Patients. Am J Respir Crit Care Med. 2021;204(7):859–62. pmid:34181862
- 33. Boyce MR, Katz R. Risk Factors for Infectious Diseases in Urban Environments of Sub-Saharan Africa: A Systematic Review and Critical Appraisal of Evidence. Trop Med Infect Dis. 2019;4(4). pmid:31569517
- 34. Tian T, Zi X, Peng Y, Wang Z, Hong H, Yan Y, et al. H3N2 influenza virus infection enhances oncostatin M expression in human nasal epithelium. Exp Cell Res. 2018;371(2):322–9. pmid:30142324
- 35. Brook I. Microbiology of chronic rhinosinusitis. Eur J Clin Microbiol Infect Dis. 2016;35(7):1059–68. pmid:27086363
- 36. Bröms K, Norbäck D, Eriksson M, Sundelin C, Svärdsudd K. Effect of degree of urbanisation on age and sex-specific asthma prevalence in Swedish preschool children. BMC public health. 2009;9:303. pmid:19695101
- 37. Vargas MH, Sienra-Monge JJ, Díaz-Mejía G, DeLeón-González M. Asthma and geographical altitude: an inverse relationship in Mexico. J Asthma 1999;36(6):511–7. pmid:10498046
- 38. Rijssenbeek-Nouwens LH, Fieten KB, Bron AO, Hashimoto S, Bel EH, Weersink EJ. High-altitude treatment in atopic and nonatopic patients with severe asthma. Eur Respir J. 2012;40(6):1374–80. pmid:22441741
- 39. Grissom CK, Jones BE. Respiratory Health Benefits and Risks of Living at Moderate Altitude. High Alt Med Biol. 2018;19(2):109–15. pmid:28375663
- 40. Jaume F, Valls-Mateus M, Mullol J. Common Cold and Acute Rhinosinusitis: Up-to-Date Management in 2020. Curr Allergy Asthma Rep. 2020;20(7):28. pmid:32495003
- 41. Wee JH, Min C, Jung HJ, Park MW, Park B, Choi HG. Association between air pollution and chronic rhinosinusitis: a nested case-control study using meteorological data and national health screening cohort data. Rhinology. 2021;59(5):451–9. pmid:34472546
- 42. Elam T, Raiculescu S, Biswal S, Zhang Z, Orestes M, Ramanathan M. Air Pollution Exposure and the Development of Chronic Rhinosinusitis in the Active Duty Population. Mil Med. 25;187(9–10):e1247. pmid:35015888
- 43. Goh LC, Shakri ED, Ong HY, Mustakim S, Shaariyah MM, Ng WSJ, et al. A seven-year retrospective analysis of the clinicopathological and mycological manifestations of fungal rhinosinusitis in a single-centre tropical climate hospital. J Laryngol Otol. 2017;131(9):813–6. pmid:28841131
- 44. Wang LL, Chen FJ, Yang LS, Li JE. Analysis of pathogenetic process of fungal rhinosinusitis: Report of two cases. World J Clin Cases. 2020;8(2):451–63. pmid:32047798
- 45. Rowan NR, Storck KA, Schlosser RJ, Soler ZM. The Role of Home Fungal Exposure in Allergic Fungal Rhinosinusitis. Am J Rhinol Allergy. 2020;34(6):784–91. pmid:32539434