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JRUMS 2025, 24(2): 141-153 |
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Ethics code: IR.UMSHA.REC.1401.393
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Moradi ّ, Darabi F, Mirzaei M, Moradi N. Spatial-Temporal Clustering Analysis of COVID-19 Mortality in Hamadan Province: An Ecological Study. JRUMS 2025; 24 (2) :141-153
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URL: http://journal.rums.ac.ir/article-1-7547-en.html
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Spatial-Temporal Clustering Analysis of COVID-19 Mortality in Hamadan Province: An Ecological Study
Ali Moradi[1], Fatemeh Darabi[2], Mohammad Mirzaei[3], Nastaran Moradi[4]
Received: 06/11/24 Sent for Revision: 15/01/25 Received Revised Manuscript: 19/04/25 Accepted: 20/04/25
Background and Objectives: Information related to the spread of disease and spatial risk factors can assist policymakers and managers in disease control. This study aimed to analyze the spatial-temporal clustering of COVID-19 mortality in Hamadan Province.
Materials and Methods: The present study is an ecological research. All patients with a definitive diagnosis of COVID-19 and death due to COVID in Hamadan Province from the beginning of 2020 to the end of 2022 were included in the study. Zoning maps were used to display the distribution of death cases.The Getis-Ord G statistic was utilized for hot spot analysis. To examine the trend of COVID-19 mortality over the course of the epidemic, a Poisson model was employed.
Results: In total, 7265 COVID-19 patients who died during the study period were included. In November 2020, and May and September 2021, the highest cases of COVID-19 related deaths occurred, coinciding with the peaks of the third, fourth, and fifth waves of the epidemic. In each of the studied periods, COVID-19 mortality significantly changed compared to the baseline period (from March to September 2020). The relative risk of death due to COVID-19 in the province increased from October to February 2020 and March to September 2021 compared to the baseline period and decreased in subsequent periods. High-risk points in Hamadan Province regarding the incidence of deaths caused by COVID-19 from March 2020 to February 2022 were located in several northern and central counties of the province.
Conclusion: At the beginning of the epidemic, high-risk areas with high incidence rates were located in the northern counties of the province. In later stages, during the middle of the epidemic, the range of high-risk areas expanded to the central-southern and then southern counties of the province.
Keywords: COVID-19, Geographic information system, Spatial-temporal clustering analysis, Hamadan
Funding: This study did not have any funds.
Conflict of interest: None declared.
Ethical considerations: The Ethics Committee of Hamadan University approved the study (IR.UMSHA.REC.1401.393).
Authors’ contributions:
Ali Moradi[1], Fatemeh Darabi[2], Mohammad Mirzaei[3], Nastaran Moradi[4]
Received: 06/11/24 Sent for Revision: 15/01/25 Received Revised Manuscript: 19/04/25 Accepted: 20/04/25
Background and Objectives: Information related to the spread of disease and spatial risk factors can assist policymakers and managers in disease control. This study aimed to analyze the spatial-temporal clustering of COVID-19 mortality in Hamadan Province.
Materials and Methods: The present study is an ecological research. All patients with a definitive diagnosis of COVID-19 and death due to COVID in Hamadan Province from the beginning of 2020 to the end of 2022 were included in the study. Zoning maps were used to display the distribution of death cases.The Getis-Ord G statistic was utilized for hot spot analysis. To examine the trend of COVID-19 mortality over the course of the epidemic, a Poisson model was employed.
Results: In total, 7265 COVID-19 patients who died during the study period were included. In November 2020, and May and September 2021, the highest cases of COVID-19 related deaths occurred, coinciding with the peaks of the third, fourth, and fifth waves of the epidemic. In each of the studied periods, COVID-19 mortality significantly changed compared to the baseline period (from March to September 2020). The relative risk of death due to COVID-19 in the province increased from October to February 2020 and March to September 2021 compared to the baseline period and decreased in subsequent periods. High-risk points in Hamadan Province regarding the incidence of deaths caused by COVID-19 from March 2020 to February 2022 were located in several northern and central counties of the province.
Conclusion: At the beginning of the epidemic, high-risk areas with high incidence rates were located in the northern counties of the province. In later stages, during the middle of the epidemic, the range of high-risk areas expanded to the central-southern and then southern counties of the province.
Keywords: COVID-19, Geographic information system, Spatial-temporal clustering analysis, Hamadan
Funding: This study did not have any funds.
Conflict of interest: None declared.
Ethical considerations: The Ethics Committee of Hamadan University approved the study (IR.UMSHA.REC.1401.393).
Authors’ contributions:
- Conceptualization: Ali Moradi
- Methodology: Nastaran Moradi
- Data collection: Mohammad Mirzaei
- Formal analysis: Ali Moradi, Nastaran Moradi
- Supervision: Ali Moradi, Nastaran Moradi, Mohammad Mirzaei
- Project administration: Nastaran Moradi
- Writing – original draft: Fatemeh Darabi, Ali Moradi, Nastaran Moradi
[1]- PhD in Epidemiology, Occupational Health and Safety Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
[2]- PhD in Health Education and Promotion, Dept. of Public Health, Asadabad School of Medical Sciences, Asadabad, Iran
[3]- Dept. of Epidemiology, School of Health, Hamadan University of Medical Sciences, Hamadan, Hamadan, Iran
[4]- Dept. of Nutrition, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran, ORCID: 0009-0007-2191-2278
(Corresponding Author) Tel: 09930485540, E-mail: moradi.nastaran80@gmail.com
(Corresponding Author) Tel: 09930485540, E-mail: moradi.nastaran80@gmail.com
Type of Study: Research |
Subject:
Environmental Health
Received: 2024/10/29 | Accepted: 2025/04/20 | Published: 2025/05/20
Received: 2024/10/29 | Accepted: 2025/04/20 | Published: 2025/05/20
References
1. Hawkins RB, Charles EJ, Mehaffey JH. Socio-economic status and COVID-19–related cases and fatalities. Public Health 2020; 189: 129-34.
2. Messner W. The institutional and cultural context of cross-national variation in COVID-19 outbreaks. Medrxiv. 2020.
3. Collivignarelli MC, Abbà A, Bertanza G, Pedrazzani R, Ricciardi P, Miino MC. Lockdown for CoViD-2019 in Milan: What are the effects on air quality? Science of the total environment. 2020; 732: 139280.
4. Strang P, Fürst P, Schultz T. Excess deaths from COVID-19 correlate with age and socio-economic status. A database study in the Stockholm region. Upsala Journal of Medical Sciences 2020; 125(4): 297-304.
5. Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet 2020; 395(10223): 514-23.
6. WHO Covid-19 Dashboard, available at https://data.who.int/dashboards/covid19/cases?n=o
7. Garmarodi G, Moradi A. Designing a tool to measure the economic-social situation in Tehran. Payesh 2010; 2(9): 137-44.
8. Hilbe JM. Negative binomial regression: Cambridge University Press; 2011.
9. Kanga S, Sudhanshu, Meraj G, Farooq M, Nathawat MS, Singh SK. Reporting the management of COVID-19 threat in India using remote sensing and GIS based approach. Geocarto International 2020 17: 1-8.
10. Ahasan R, Alam MS, Chakraborty T, Hossain MM. Applications of GIS and geospatial analyses in COVID-19 research: a systematic review. F1000Research. 2020; 9(1379): 1379.
11. Ma Y, Zhao Y, Liu J, He X, Wang B, Fu S, et al. Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Science of the Total Environment 2020; 724: 138226.
12. Ahmed F, Ahmed Ne, Pissarides C, Stiglitz J. Why inequality could spread COVID-19. The Lancet Public Health 2020; 5(5): e240.
13. Hasan NA, Haque MM. Predict the next moves of COVID-19: reveal the temperate and tropical countries scenario. medRxiv. 2020.
14. Rahman M, Zafri NM, Ashik F, Waliullah M. GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh. Niaz Mahmud and Ashik, Fajle and Waliullah, Md, Gis-Based Spatial Modeling to Identify Factors Affecting COVID-19 Incidence Rates in Bangladesh (August 16, 2020). 2020.
15. Neşe A, Bakir H. Spatiotemporal analysis of COVID-19 in Turkey. Sustainable Cities and Society 2022; 76: 103421.
16. Arab-Mazar Z, Sah R, Rabaan AA, Dhama K, Rodriguez-Morales AJ. Mapping the incidence of the COVID-19 hotspot in Iran–Implications for Travellers. Travel Medicine and Infectious Disease 2020; 34: 101630.
17. Pordanjani SR, Mohammadian M, Derakhshan S, Hadavandsiri F, Nazari SSH, Panahi MH. Spatio-temporal Analysis of COVID-19: A Global Study. Middle East Journal of Rehabilitation and Health Studies 2023; 10(2).
18. Shakiba M, Nazari SSH, Mehrabian F, Rezvani SM, Ghasempour Z, Heidarzadeh A. Seroprevalence of COVID-19 virus infection in Guilan province, Iran. medRxiv. 2020.
19.
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