Geospatial modelling to understand malarial prevalence

In the previous article, a trend-based assessment was conducted to show the prevalence of Malaria in India. Geospatial modelling is another form of trend based pictorial assessment used by the healthcare and pharmaceutical industry. This article presents state-wise malaria incidence data to test the patterns of prevalence of malaria in India between 2011 and 2015. Geospatial  modelling helps in comparing the intervention efficacy with respect to different states in India and the aspect that contribute to its inefficacy. 

Geospatial modelling helps to understand the impact of health issues on the population. Epidemiology studies use geographic information system (GIS) as it is a very efficient way of disease surveillance and intervention monitoring method. This approach is highly useful in case of determining the affected geographical space. This further helps in planning intervention methods and also monitor the effectiveness of the plan (Musa et al., 2013). Pharmaceutical industries use GIS data to understand the efficacy of drugs. In addition, the data helps to study a particular strain or genotype of malarial infections endemic to the geographic location. Healthcare industries use GIS data to implement awareness and advanced treatment methods specific to the disease prevalent geographical locations.

Quantitative Geographic Information Software (QGIS)

Quantitative Geographic Information Software (QGIS) is used in geospatial modelling of the prevalence of malaria in the Indian state (QGIS, 2018). State-wise malaria incidences data was collected in India, between the period 2011 to 2015 (National Health Ministry, 2016). Then, the map of India was imported into the QGIS software along with the state boundaries.

The state-wise data for the number of cases of malaria got integrated and represented the maps using the features of QGIS, to depict the prevalence of malaria in states of India. Colour coding in the QGIS comprised of lighter green colour representing the low number of incidence cases. Increasing intensity of green colour means the number of incidence cases increases. Furthermore, numerical codes against colour codes for zone-wise interpretation were given as 0-4.

Color coding

Colour coding

The maps of India of every year is representing the prevalence of malaria in the country.

  • Colour-coded states according to cases of malaria in those particular states or zones
  • The shades of green depict the intensity malaria incidences.
  • Higher the malarial incidence cases darker shade of green is used while lower malarial incidence cases are depicted by the lighter shades of green
  • Total of 5 classes on the basis of graduated format is there for every year to maintain uniformity in the maps of each year.
Geospatial modelling of malaria in the states of India from the year 2011-2015

Geospatial modelling of malaria in the states of India from the year 2011-2015

Zone-based distribution of prevalence of malaria

East Zone West Zone South Zone Central Zone North Zone
2011  3  4  3  4  3
2012  4  4  2  4  3
2013  4  4  2  4  4
2014  3  4  3  4  4
2015  4  4  3  4  4

Malaria prevalence remains highest in central India i.e. Madhya Pradesh, Maharashtra, Chhattisgarh, Jharkhand, and Odisha (n=20000 to 50000) from 2011 to 2015.  The results also reveal that the rate of prevalence is decreasing in some of the states like Tamil Nadu, Tripura, Punjab, and a few others. The states at higher elevation i.e. Jammu and Kashmir, Himachal Pradesh, Uttarakhand, and Punjab, have the least cases of malaria in all the years (n=0 to 200) throughout 2011-2015.

The incidence rate in some states, for example, Northeastern states is ascending. The reduction in the incidence cases in the northeastern states is due to effective implementation of vector and disease management programs (IRIS, 2016). However, climatic conditions like heavy rainfall cause the disease to re-emerge. Therefore, they require a more effective control measure to combat the disease prevalence.

A geospatial modelling to depict the malarial trend

High rates of malaria cases were found in the states with the majority a majority of the tribal population residing in these states collectively i.e. Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Gujarat, Jharkhand, Chhattisgarh, Andhra Pradesh, West Bengal and Karnataka (Sharma et al., 2015). This is due to the improper health care infrastructure (IRIS, 2016).  The higher prevalence of malaria in the states of middle India like Madhya Pradesh, Chhattisgarh, Maharashtra, and Uttar Pradesh is due to the geographical conditions like tropical climatic conditions of these states i.e. high rainfall and high humidity (National Institute of Malaria research, 2009).

Due to the higher rainfall in these states, it becomes the ideal ground for breeding places for Anopheles mosquitoes. Thus, area, environment, and man-made causes are the most important factors that cause the prevalence of malaria. GDP per capita of the states has caused a poor geospatial malarial trend. In addition, illiteracy is very poor in these prevalent states, and the implications to improve literacy is a major setback (Hooda, 2015). Higher literacy rate in a society helps to improve health awareness and increase the utilization of the healthcare service. The prevalence of malaria from the geospatial assessments of Indian states indicates appropriate implications of the healthcare policy.

The prevalence of malaria cases shows the efficacy of the state governments of India towards control and management (Hooda, 2015). The GIS findings also indicate the efficacy of utilizing the expenditures set by the Indian government. Only a few states seem have been efficiently decreasing the cases of malaria incidences with efficient use of healthcare expenditure data. Hooda, (2015) also indicates that the states are improving with respect to GDP per capita, literacy rate, the degree of urbanization and the number of physicians in the state or district.

References

  • Hooda, S.K., 2015. Determinants of Public Expenditure on Health In India: The Panel Data Estimates. Retrieved from http://isid.org.in/pdf/WP177.pdf
  • IRIS. (2016). Seasonal Migrations of Marginalized (Tribal) Communities in Madhya Pradesh & Rajasthan: Foresight Analysis and Scenarios by 2020: Key observations, (September). Retrieved from http://www.iris-france.org/wp-content/uploads/2016/11/ENG-Observatoire-Prospective-Huma-Seasonal-Migration-India-Septembre-2016.pdf.
  • Musa, G. J., Chiang, P.-H., Sylk, T., Bavley, R., Keating, W., Lakew, B., … Hoven, C. W. (2013). Use of GIS Mapping as a Public Health Tool—From Cholera to Cancer. Health Services Insights, 6, 111–116. https://doi.org/10.4137/HSI.S10471.
  • National Health Ministry of India. (2016). Disease Control Programmes [ NHM ].
  • National Institute of Malaria research. (2009). Estimation of true malaria burden in India. … . 2nd Ed New Delhi, India: National Institute of Malaria …, 91–99.
  • QGIS. (2018). QGIS: A free and open source geographic information system. Retrieved August 13, 2018, from https://www.qgis.org/en/site/.
  • Sharma, R. K., Thakor, H. G., Saha, K. B., Sonal, G. S., Dhariwal, A. C., & Singh, N. (2015). Malaria situation in India with special reference to tribal areas. The Indian Journal of Medical Research, 141(5), 537–545. https://doi.org/10.4103/0971-5916.159510.
Avishek Majumder

Avishek Majumder

Research Analyst at Project Guru
Avishek is a Master in Biotechnology and has previously worked with Lifecell International Private Limited. Apart from data analysis and biological research, he loves photography and reading. He loves to play football and basketball in his spare time with an avid interest in adventure and nature. He was also a member of the Scouts in his school and has attended Military training.
Avishek Majumder

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