Analysis of spatial distribution and risks of respiratory diseases in Tehran

Document Type : thesis

Authors

1 Geography and Urban Planning, Faculty of Social Sciences, university of Mohaghegh Ardabili , Ardabil, Iran

2 Department of Geography and Urban Planning, university of Mohaghegh Ardabili , Ardabil,

3 Department of Geography and Urban Planning, university of Mohaghegh Ardabili , Ardabil, Iran

Abstract

Today, respiratory diseases in the form of infectious diseases and diseases caused by environmental issues are spreading day by day, and according to the statistics of the World Health Organization, they have a significant rank among human diseases. The metropolis of Tehran has been exposed to the increasing incidence of respiratory diseases with the increasing growth of its population and urban area as well as air pollution. Analyzing spatio-temporal patterns of respiratory diseases using GIS is very important in understanding the geographical distribution as well as epidemiological and health studies of the urban community of Tehran. This applied and descriptive-analytical research has done spatio-temporal analysis and spatial distribution modeling of the epidemiology of respiratory diseases and their risks in Tehran using spatial statistics. The results of spatial autocorrelation showed that districts 13 and 14 in Tehran are located in the HH cluster, which constitutes 9.09% of all districts. Also, areas 4, 8, 13, 14, and 15 are located in hot spots, which constitute 22.72% of the total areas of Tehran. Investigations showed that the factors of distance and proximity to areas involved in respiratory diseases are among the most important causes of air spread of respiratory diseases in the city of Tehran, which follows the pattern of air spread.

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  1. اثماریان، نعیمه السادات؛ کاوسی، امیر؛ صالحی، مسعود(1392). تنظیم نقشه جغرافیایی میزان بروز سرطان روده بزرگ در ایران طی سال های 1386-1382، با استفاده از روش کریگیدن پواسنی منطقه به منطقه، مجله علوم پزشکی رازی، دوره 40، شماره 107، صص 17-10.
  2. اعتمادی، ملیحه(1398). فیزیوتراپی در بیماری‌های تنفسی. انتشارات قلم علم.
  3. فرجی سبکبار، حسنعلی؛ ایرنخواه، احمد؛ مونی، حسن(1397). تحلیل فضایی آسیب‌پذیری اجتماعی در مراکز پیراشهری مورد مطالعه حصارک کرج، فصلنامه جغرافیا و روابط انسانی، دوره1، شماره1، صص 680-662.
  4. کرمانی، مجید؛ آقائی، مینا؛ بهرامی اصل، فرشاد؛ غلامی، میترا؛ فلاح، سودا؛ دولتی، محسن؛ کریم زاده، سیما(1395). برآورد تعداد موارد مرگ قلبی-عروقی، سکته قلبی و بیماری مزمن انسداد ریوی ناشی از تماس با آلاینده دی اکسیدگوگرد در هوای شش شهر صنعتی ایران، مجله علوم پزشکی رازی، دوره 23، شماره 14، صص 21-12.
  5. قدمی، مصطفی؛ دیوسالار، اسداله؛ رنجبر، زینت؛ غلامیان آقامحلی؛ طاهره(1392). ارزیابی راهبردی ساختار فضایی شهر در چارچوب پایداری(مطالعه موردی شهر ساری)، فصلنامه اقتصاد و مدیریت شهری، شماره سوم، صص 16-1.
  6. Li, X., Cao, X., Guo, M., Xie, M., & Liu, X. (2020). Trends and risk factors of mortality and disability adjusted life years for chronic respiratory diseases from 1990 to 2017: systematic analysis for the Global Burden of Disease Study 2017. bmj, 368.
  7. Xie, M., Liu, X., Cao, X., Guo, M., & Li, X. (2020). Trends in prevalence and incidence of chronic respiratory diseases from 1990 to 2017. Respiratory research, 21(1), 1-13.
  8. Bhowmik, R. T., & Most, S. P. (2022). A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks. Electronics11(16), 2562.
  9. Bertaud, A. (2003). Tehran spatial structure: Constraints and Opportunities for Future Development National Land and Housing Organization, National Housing Committee Ministry of Housing and Urban Development, Islamic Republic of Iran.
  10. Kandwal, R., Garg, P. K., & Garg, R. D. (2009). Health GIS and HIV/AIDS studies: Perspective and retrospective. Journal of biomedical informatics, 42(4), 748-755.
  11. American Health Organization.(1996). Use of GIS in epidemiology. Epidemiological Bulletin; 17:1-7.
  12. Rezaeian M.(2007). Geographical epidemiology, spatial analysis& geographical information system: a multidisciplinary glossary. J Epidemiol Community Health; 61: 98-102.
  13. Wu, F., Zhao, S., Yu, B., Chen, Y. M., Wang, W., Song, Z. G., ... & Zhang, Y. Z. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579(7798), 265-269.
  14. Bailley T, Gatrell A.(1995). Interactive spatial data analysis. 1st ed. Harlow. Longman.
  15. Alvarez-Mendoza, C., Teodoro, A., Freitas, A., Fonseca, J.(2020). Spatial estimation of chronic respiratory diseases based on machine learning procedures—an approach using remote sensing data and environmental variables in quito, Ecuador, Journal of Applied Geography 123 (2020) 102273.
  16. Durand, M., Wilson, J. G.(2006). Spatial analysis of respiratory disease on an urbanized geothermal field, Journal of Environmental Research 101 (2006) 238–245.
  17. Farah, C., Hosgood, H., M. Hock, J.(2014). Spatial prevalence and associations among respiratory diseases in Maine, Journal of Spatial and Spatio-temporal Epidemiology 11 (2014) 11–22.
  18. Kelly, G. C., Tanner, M., Vallely, A., & Clements, A. (2012). Malaria elimination: moving forward with spatial decision support systems. Trends in parasitology, 28(7), 297-304.