This study used geographic information systems and spatial statistics to analyze malaria data from 2007-2009 in Ranchi district, India. The analysis identified hot spots and clustering of Plasmodium falciparum (Pf) infections, but not Plasmodium vivax (Pv) infections. A consistent Pf hot spot was detected in 5 subcenters. The analysis showed a shifting trend in Pf spread towards the west over time. Priority control measures were recommended for the identified Pf hot spots and high risk areas.

1. A spatial statistical approach to analyze
malaria situation at micro level for priority
control in Ranchi district, Jharkhand
Rekha Saxena1, B.N. Nagpal1,, Aruna Srivastava1, Sanjeev Kumar
Gupta 1,Anil Kumar 1, M.K. Das2 ,A.T. Jeyaseelan3 & Vijay Kumar
Baraik4
1) National Institute of Malaria Research (ICMR), New Delhi
2) IDVC Project Field Unit(Under National Institute of Malaria Research), Ranchi
3) Jharkhand Space Application Center, Ranchi & 4) School of Sciences (SoS),
Indira Gandhi National Open University (IGNOU), New Delhi, India
Indian J Med Res 136, November 2012, pp 776-782

2. About The Journal
• Scope : Technical and clinical studies related to health, ethical and social issues
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• Editor : Dr Anju Sharma

3. Introduction
• Malaria cases increased 0.21 million in 2008 to 0.23 million
in 2009. (Pf cases from 34 to 40 per cent) - Jharkhand
• Ranchi District –
 endemic for malaria.
 14 PHCs and 328 subcentres
 Only 35 per cent of population live in urban area
 Malaria vectors Anopheles culicifacies, An. fluviatilis and
An. Annularis are rampant

4. Intro…
• The global maps prepared for malaria risk distribution, at
continental/country level under supports control programmes at
local scale.
• Geographical information system (GIS) + spatial statistical tools
analyze the epidemiological data at local level by detecting spatial
patterns of disease distribution and delineation of hot spots
• GIS based retrospective study was initiated in 328 subcentres of
14 (PHCs) of Ranchi district, Jharkhand, India, using malaria
epidemiological data of three years (2007-2009).

5. Aims & Objectives
• To identify spatial distribution patterns of
Plasmodium vivax/ P. falciparum (Pv /Pf) occurrence
• Delineation of hot spots
• To map directional distribution trend of Pf spread
during 2007-2009

6. Materials & Methods
• Type of study: Retrospective Study (2007-2009)
• Spatial Data: PHC & Village wise maps of Ranchi district
 Universal Transverse Mercator (WGS-84) - Scale1: 50000
 Projection System -Jharkhand Space Application Centre , Ranchi
 Villages falling in a subcentre were assigned the same code
 ArcGIS 9.3, USA Geo Processing tool
 Avg Aerial distance btw subcentres – 5-10 kms (Distance measuring tool)

7. Materials….
Malaria Epidemiological data
 Annual malaria epidemiological data (2007-2009) for 328
subcentres of Ranchi district.
 DATA : Population, blood slides and the number of Pv and Pf
positive cases.
 PvR = Pv positive cases x 1000 / Subcentre population
 PfR = Pf positive cases x 1000 / Subcentre population
 Average Pv/Pf rates as APvR and APfR were used to analyse
overall situation

8. HOT SPOT Categories

9. Geographic Information system (GIS)
• A geographic information system (GIS) is a computer-based tool for
mapping and analyzing spatial data.
• Environmental Systems Research Institute (ESRI) - An organized
collection of computer hardware, software, geographic data and personnel
designed to efficiently capture, store, update, manipulate, analyze and
display geographically referenced information.
• The United States Geological Survey (USGS) - A Computer hardware and
software system designed to collect, manage, analyze and display
geographically (spatially) referenced data.
• Its major advantage is that it permits identifying spatial relationships
between specific different map features

10. Geographic Information system
GIS = G + IS
Geographic reference Information system
Data of spatial coordinates
on the surface of the earth
(Map) –location data
Database of attribute data
corresponding to spatial
location and procedures to
provide information for
decision making
GIS = IS with geographically referenced data

11. Global Moran’s I Index
• GIS and spatial statistical tools:
 Spatial autocorrelation analysis using Global Moran’s I Index was used
to identify the spatial pattern which may be clustered, dispersed or
random based on feature locations and attribute values simultaneously
 ‘Z’ value is calculated to assess whether the observed
clustering/dispersing is statistically significant or not.
 The value of Moran’s I Index is between -1 and 1
 + ve Moran’s I index value indicates tendency toward clustering ,
 - ve Moran’s I index value indicates tendency toward dispersion
 When its 0 there is no spatial autocorrelation and the spatial pattern is
considered to be random

12. Getis- Ord Gi*
• Getis-Ord Gi* statistics identifies different spatial clustering patterns
like hot spots, high risk and cold spots over the entire study area
with statistical significance.
• The statistic returns a Z score for each feature in the dataset. For
statistically significant positive Z score, the larger the Z score is, the
more intense the clustering of high values (hot spots).
• For statistically significant negative Z score, the smaller the Z score
is, the more intense the clustering of low values (cold spots).
• High risk areas are at lower significance level in comparison to hot
spots.

13. Standard Deviational Ellipse (SDE)
• SDE was used to map the directional distribution trend of Pf
spread during the three years.
• SDE measures whether features are farther from a specified
point in one direction than in another direction.
• Trend line indicated upward/ downward trend of the
subcentres under hot spots/high risk category during the
three years
• Overlaying of hot spots for three years was done in GIS to
identify consistent hot spots.

14. Results

15. Mapping of Pf hot spots, high risk & cold spots using
Getis- Ord Gi statistics during 2007-2009 in subcentres of
Ranchi district

17. Trends of Pf subcentres under hot spot and high risk
categories during 2007-2009 in Ranchi district, Jharkhand,
India.

18. Subcentres in Pf hot spot and high risk categories in Ranchi
district, Jharkhand, India during 2009

19. Consistent Pf hot spot in Silli PHC consisting of 5 subcentres
(Goradih, Jaradih, Piska, Rampur and Sillidih) during 2007-2009
in Ranchi district, Jharkhand, India.

20. Results contd…
• Moran’s I index for APvR was found to be -0.01 with Z score being -0.79
which is non-significant - random pattern of Pv distribution
• Moran’s I index for APfR was found to be 0.1 with Z score being 6.46 which
is significant at 99% confidence level (P<0.01) - overall clustering pattern.
• Moran’s I indices for PfR during 2007, 2008 and 2009 were found to be 0.09,
0.04 and 0.11 with Z scores being 6.16, 3.42 and 7.27 which are significant
at 99% confidence level (P<0.01) - clustering pattern of Pf for each year
• The null hypothesis is accepted for Pv while it is rejected for Pf.

21. Results contd..
• The number of subcentres under Pf hot spot category exhibited
downward trend while high Pf risk subcentres exhibited upward trend
from 2007 to 2009
• During 2009, Pf hot spot consisting of 20 subcentres was identified of
which five were already listed.
• 18 High Pf risk subcentres were indicated in 2009, These hot spots
and high risk subcentres were located in 4 PHCs namely Angara,
Silli, Burmu and Kanke

22. Standard Deviational Ellipse indicating trend of Pf spread
during 2007-2009 in Ranchi district, Jharkand, India

23. Results Contd
• SDE - Shifting trend in Pf spread from north-west to western
direction from 2008 onwards
• It was observed that mostly high Pf risk and adjacent subcentres
converted to hot spots in subsequent years.
• Priority control recommended in 20 Pf hot spot and 18 high Pf risk
reporting subcentres including five consistent Pf hot spot
subcentres in Angara, Silli, Burmu and Kanke PHCs during 2011

24. Results…
• Strengthening of surveillance with early detection and complete
treatment (EDCT) was recommended in low Pf occurrence
areas identified in Ratu and Kanke PHCs during 2009 to avert
outbreak of disease.
• Shifting trend in Pf spread from 2008 onwards towards western
direction was indicated to State Health Department to consider
change in the existing control policies.

25. Conclusion
• Study highlighted the utility of GIS and spatial statistical
tools in efficient processing of voluminous epidemiological
data at micro (subcentre) level.
• Study established the role of GIS in disease control
• Rapid and readily understandable results were provided
which helped in quick decision making

26. Limitation
• The risk factors of Pv / Pf occurrence and clustering
of Pf leading to formation of hot spots/high risk
pockets in the PHCs of the district were not
investigated.

27. THANK YOU