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ORIGINAL ARTICLE
Year : 2020  |  Volume : 13  |  Issue : 4  |  Page : 162-168

Provincial clustering of malaria in Iran between 2005 and 2014


1 Department of Biostatistics & Epidemiology, School of Health, Mazandaran University of Medical Sciences, Sari, Iran
2 Medical Entomology, Mazandaran University of Medical Sciences, Sari, Iran
3 Department of Medical Entomology and Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Environmental Chemical Pollutants and Pesticides, Institute for Environmental Research, Tehran University of Medical Sciences, Tehran, Iran
5 Department of Biostatistics, Health Sciences Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Iran

Correspondence Address:
Jamshid Yazdani Charati
Department of Biostatistics, Health Sciences Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1995-7645.280223

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Objective: To reveal the provincial clustering of malaria in Iran between 2005 and 2014 based on the epidemiologic factors and the climatic indicators affecting the disease. Methods: This was a descriptive-analytical study using malaria and meteorological data from the Malaria Elimination Programme of the Ministry of Health and Medical Education and National Meteorological Organization. After standardization, the aggregate data was used to produce 10-year means for each province. The data analysis included grouping the provinces with respect to factors using hierarchical clustering method and Kruskal-Wallis test to examine the difference between clusters using SPSS ver.23. Results: The hierarchical clustering stratified the provinces’ in 5 clusters. Kruskal-Wallis H test revealed a significant difference in the incidence rate per 100 000 population (P=0.001), male gender (P=0.001), Iranian nationality (P=0.001), Afghan nationality (P=0.003), Pakistani nationality (P=0.001), urban residence (P=0.006), rural residence (P=0.004), autochthonous cases (P=0.007), average minimum temperature (P=0.001), average maximum temperature (P=0.007), average relative humidity (P=0.011), average pressure level (P=0.038), prevailing wind direction (P=0.023), average wind speed (P=0.031) and average precipitation sum (P=0.002) among the clusters. Conclusions: The results of this study and stratification of the provinces could help health policy makers to better manage malaria by allocating resources accordingly.


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