Malaria Prone Zones of West Bengal: A Spatio-Temporal Scenario
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
Malaria Prone Zones of West Bengal: A Spatio-Temporal Scenario

Authors: Meghna Maiti, Utpal Roy

Abstract:

In India, till today, malaria is considered to be one of the significant infectious diseases. Most of the cases regional geographical factors are the principal elements to let the places a unique identity. The incidence and intensity of infectious diseases are quite common and affect different places differently across the nation. The present study aims to identify spatial clusters of hot spots and cold spots of malaria incidence and their seasonal variation during the three periods of 2012-2014, 2015-2017 and 2018-20 in the state of West Bengal in India. As malaria is a vector-borne disease, numbers of positive test results are to be reported by the laboratories to the Department of Health, West Bengal (through the National Vector Borne Disease Control Programme). Data on block-wise monthly malaria positive cases are collected from Health Management Information System (HMIS), Ministry of Health and Family Welfare, Government of India. Moran’s I statistic is performed to assess the spatial autocorrelation of malaria incidence. The spatial statistical analysis mainly Local Indicators of Spatial Autocorrelation (LISA) cluster and Local Geary Cluster are applied to find the spatial clusters of hot spots and cold spots and seasonal variability of malaria incidence over the three periods. The result indicates that the spatial distribution of malaria is clustered during each of the three periods of 2012-2014, 2015-2017 and 2018-20. The analysis shows that in all the cases, high-high clusters are primarily concentrated in the western (Purulia, Paschim Medinipur districts), central (Maldah, Murshidabad districts) and the northern parts (Jalpaiguri, Kochbihar districts) and low-low clusters are found in the lower Gangetic plain (central-south) mainly and northern parts of West Bengal during the stipulated period. Apart from this seasonal variability inter-year variation is also visible. The results from different methods of this study indicate significant variation in the spatial distribution of malaria incidence in West Bengal and high incidence clusters are primarily persistently concentrated over the western part during 2012-2020 along with a strong seasonal pattern with a peak in rainy and autumn. By applying the different techniques in identifying the different degrees of incidence zones of malaria across West Bengal, some specific pockets or malaria hotspots are marked and identified where the incidence rates are quite harmonious over the different periods. From this analysis, it is clear that malaria is not a disease that is distributed uniformly across the state; some specific pockets are more prone to be affected in particular seasons of each year. Disease ecology and spatial patterns must be the factors in explaining the real factors for the higher incidence of this issue within those affected districts. The further study mainly by applying empirical approach is needed for discerning the strong relationship between communicable disease and other associated affecting factors.

Keywords: Malaria, infectious diseases, spatial statistics, spatial autocorrelation, LISA.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 418

References:


[1] R. P. Misra, Geography of Health: A Treatise on Geography of Life and Death in India. Concept Publishing Company, 2007.
[2] A. P. Dash, T. Jain, A. Kumar, and N. Valecha, “Burden of Malaria in India: Retrospective and Prospective View,” The American Journal of Tropical Medicine and Hygiene, vol. 77, no. 6_Suppl, pp. 69–78, Dec. 2007, doi: 10.4269/ajtmh.2007.77.69.
[3] “Findings from the Global Burden of Disease Study 2017,” Institute for Health Metrics and Evaluation, Jan. 04, 2019. https://www.healthdata.org/policy-report/findings-global-burden-disease-study-2017 (accessed Mar. 22, 2023).
[4] WHO, World Malaria Report 2018. WHO Regional Office for the Western Pacific, 2019.
[5] ICMR, PHFI, IHME, and DHR, MHFW, “India: Health of the Nation’s States The India State-Level Disease Burden Initiative,” Institute for Health Metrics and Evaluation, 2017. http://www.healthdata.org/india (accessed May 21, 2019).
[6] M. Rezaeian, G. Dunn, S. S. Leger, and L. Appleby, “Geographical epidemiology, spatial analysis and geographical information systems: a multidisciplinary glossary,” Journal of Epidemiology and Community Health (1979-), vol. 61, no. 2, pp. 98–102, 2007.
[7] M. Ali, M. Emch, C. Ashley, and P. K. Streatfield, “Implementation of a Medical Geographic Information System: Concepts and Uses,” Journal of Health, Population and Nutrition, vol. 19, no. 2, pp. 100–110, 2001.
[8] “Home | Government of India.” https://censusindia.gov.in/census.website/ (accessed Aug. 18, 2022).
[9] “Home: National Center for Vector Borne Diseases Control (NCVBDC).” https://nvbdcp.gov.in/ (accessed Aug. 17, 2022).
[10] “HMIS-Health Management Information System.” https://hmis.nhp.gov.in/#!/ (accessed Aug. 18, 2022).
[11] L. Anselin and A. Getis, “Spatial statistical analysis and geographic information systems,” Ann Reg Sci, vol. 26, no. 1, pp. 19–33, Mar. 1992, doi: 10.1007/BF01581478.
[12] L. Anselin, “Exploring Spatial Data with GeoDaTM: A Workbook.” Center for Spatially Integrated Social Science, 2005. Accessed: May 08, 2019. (Online). Available: http://www.csiss.org/clearinghouse/GeoDa/geodaworkbook.pdf
[13] A. Getis, “A History of the Concept of Spatial Autocorrelation: A Geographer’s Perspective,” Geographical Analysis, vol. 40, no. 3, pp. 297–309, Jul. 2008, doi: 10.1111/j.1538-4632.2008.00727.x.
[14] W. Hu, A. Clements, G. Williams, and S. Tong, “Dengue fever and El Nino/Southern Oscillation in Queensland, Australia: a time series predictive model,” Occupational and Environmental Medicine, vol. 67, no. 5, pp. 307–311, May 2010, doi: 10.1136/oem.2008.044966.
[15] A. C. Gatrell and T. C. Bailey, “Interactive spatial data analysis in medical geography,” Social Science & Medicine, vol. 42, no. 6, pp. 843–855, Mar. 1996, doi: 10.1016/0277-9536(95)00183-2.
[16] A. B. Lawson, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. CRC Press, 2018.
[17] L. Beale, J. J. Abellan, S. Hodgson, and L. Jarup, “Methodologic Issues and Approaches to Spatial Epidemiology,” Environmental Health Perspectives, vol. 116, no. 8, pp. 1105–1110, 2008.
[18] D. S. Kumar, R. Andimuthu, R. Rajan, and M. S. Venkatesan, “Spatial trend, environmental and socioeconomic factors associated with malaria prevalence in Chennai,” Malaria Journal, vol. 13, no. 1, Dec. 2014, doi: 10.1186/1475-2875-13-14.
[19] T. Bousema et al., “Identification of Hot Spots of Malaria Transmission for Targeted Malaria Control,” The Journal of Infectious Diseases, vol. 201, no. 11, pp. 1764–1774, Jun. 2010, doi: 10.1086/652456.
[20] E. Hakizimana et al., “Spatio-temporal distribution of mosquitoes and risk of malaria infection in Rwanda,” Acta Tropica, vol. 182, pp. 149–157, Jun. 2018, doi: 10.1016/j.actatropica.2018.02.012.
[21] B. Kreuels et al., “Spatial Variation of Malaria Incidence in Young Children from a Geographically Homogeneous Area with High Endemicity,” The Journal of Infectious Diseases, vol. 197, no. 1, pp. 85–93, Jan. 2008, doi: 10.1086/524066.
[22] J. M. Lauderdale et al., “Towards seasonal forecasting of malaria in India,” Malaria Journal, vol. 13, no. 1, p. 310, Aug. 2014, doi: 10.1186/1475-2875-13-310.
[23] S. R. Mutheneni, R. Mopuri, S. Naish, D. Gunti, and S. M. Upadhyayula, “Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013,” Parasite Epidemiology and Control, vol. 3, no. 1, pp. 52–61, 2016, doi: 10.1016/j.parepi.2016.11.001.
[24] A. C. Gatrell and S. J. Elliott, Geographies of Health: An Introduction. John Wiley & Sons, 2014.
[25] G. Zhou, N. Minakawa, A. K. Githeko, and G. Yan, “Association between climate variability and malaria epidemics in the East African highlands,” Proceedings of the National Academy of Sciences, vol. 101, no. 8, pp. 2375–2380, Feb. 2004, doi: 10.1073/pnas.0308714100.
[26] C. Guo et al., “Malaria incidence from 2005–2013 and its associations with meteorological factors in Guangdong, China,” Malar J, vol. 14, no. 1, p. 116, Dec. 2015, doi: 10.1186/s12936-015-0630-6.
[27] P. Bi, K. Donald, K. A. Parton, and S. Tong, “Climatic Variables and Transmission of Malaria: A 12-Year Data Analysis in Shuchen County, China,” Public Health Reports, vol. 118, p. 8, 2003.