A SaTScan STUDY TO DETECT SPATIO-
TEMPORAL CLUSTERS OF MOUTH CANCER
INCIDENCE AMONG MEN IN CHENNAI
P.Sampath1, P.R.Jayashree2, R. Srinivasan3, R. Swaminathan4
1Statistical Assistant, Department of Epidemiology and Cancer Registry, Cancer Institute (WIA),
Adyar, Chennai-20 2Assistant professor, Department of Statistics, Presidency College, Chepauk, Chennai-05
3Senior Technical Officer, National Institute for Research in Tuberculosis, ICMR, Chetpet,
Chennai-31 4Professor and Head, Department of Epidemiology and Cancer Registry, Cancer Institute (WIA),
Adyar, Chennai-20
Abstract
Background
Mouth cancer is one of the leading cancer sites among men in Chennai zones. The study examined the space time
variation in temporal trend of mouth cancer incidence during 2006-2015.
Methods
The reported mouth cancer data were obtained from Madras Metropolitan Tumour Registry (MMTR), Cancer
Institute (WIA), Chennai during 2006 - 2015. The discrete Poisson probability model is calculated by using
Kulldorff’s retrospective scan statistics was used to identify the spatio temporal clusters of mouth cancer among
men at the zone level in Chennai.
Results
A total of 2778 mouth cancer cases were reported during 2006 – 2015 and among them there were 1969 men
(70.8%) and 809 women (29.2%) cases from all zones in Chennai. Therefore in this study, the mouth cancer
incidences among men were considered. All the observed men mouth cancers were Geocoded with longitude and
Latitude coordinates. The results of Kuldorff’s scan reveal that the mouth cancer cases among men were
significantly clustered in spaio-temporal distribution. It is found that the most likely spatio-temporal cluster ( LLR
9.5134, RR=1.23,P<0.001) was found in the Basinbridge zone of Chennai.
CONCLUSIONS
This study identifies high and low significant clusters of mouth cancer among men in Chennai zones during 2006-
2015. These findings can serve for resource allocation and preventive strategies in high risk areas.
Keywords: Chennai, Gini Clusters, Mouth cancer, Pure Spatial Analysis, SaTScan, Spatio-Temporal
Analysis, Zones
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1. Introduction
Health Statistics shows that Cancer is the fourth most common cause of mortality in India. Globocan2018 reports
indicate that, oral cancer (lip, tongue and mouth) is top ranked cancer in India[5] and in particular the mouth is
predominant among men. In Chennai mouth cancer is one of the most common cancers among men and the
incidence is varied at zone level. Spatial and Space time Statistics have become popular methods in disease
surveillance for the detection of disease clusters. The detection of cluster investigation over time and space are
essential to the communal aspect to prioritize the health resources for prevention and cancer control in different
geographical area.
The incidence of cancer has been attributed to many individual-level risk factors and it has been found that are
associated with place-based and area-based risk factors addressed by Dai D. Black [4]. Most of the research studies
consider the spatial patterns of the disease at the census tract or zip code level. When studying the spatial patterns of
disease on a geographic scale, the estimated rates and observed associations might involve a degree of bias due to
spatial autocorrelation, population size heterogeneity, and small-area effects[3,4] reviewed by Alemu K et al. and
Areias C et.al .
Based on NCRP report,[16] the Age Standardized Rate (ASR) of mouth cancer in Chennai was found as 7.6 per
100,000, which is greater than the rates observed in many other districts of Tamil Nadu. Hence, this study focuses
on identifying the spatial time trend patterns and clustering of mouth cancer incidence using Kuldorff’s scan
statistics in Chennai for different zones. Thus, this study helps to understand the health needs and to optimize health
care allocation at zone level.
2. Materials and Methods
2.1 Data Sources
A total of 1969 mouth cancer cases among men were taken for this study during 2006 - 2015 in Chennai. Mouth
cancer data were obtained from Madras Metropolitan Tumour Registry. MMTR is a Population Based Cancer
Registry (PBCR) in Chennai (formerly Madras), was established at the Cancer Institute (WIA), in 1981, as part of
the National Cancer Registry Programme of the Indian Council of Medical Research, Government of India. The
study area was classified into ten zones of Chennai namely, Tondiarpet, Basinbridge, Pulianthoppu, Ayanavaram,
Kilpauk, IceHouse, Nungambakkam, Kodambakkam, Saidapet and Adyar.
2.2 Methodology
The Regression trend analysis of the ASR of mouth cancer in Chennai was done using joint point regression
program version 4.04 developed by National CancerInstitute(NCI, Boston, MA, USA). and SaTScan software
ver.9.4.4. scan statistic was used to perform space-time and purely spatial analysis. In this method, the search
window moves around the coordinates for each zone to calculate likelihood ratio and compare with the test
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hypothesis on the basis of Monte Carlo simulation test. The Discrete Poisson model was used to explore the purely
spatial and spatiotemporal clusters with high and low incidence rates of mouth cancer in all zones of Chennai.
2.2.1 Age Adjusted or Age Standardized Rate (AAR/ ASR)
The age- standardized rate was calculated using the segi’s 1960 world standard population (WSP). The ASR is
calculated by obtaining the age specific rates and applying these rates to the standard population in that age group.
aiwi
ASR or AAR = -----------
wi
Where ai- Age specific rate
wi- World Standard population
i- Age group of populations,( 0-4,5-9,10-14,….,75+)
2.2.2 Space- Time Analysis
The spatial temporal cluster detection for the incidence of mouth cancer was performed using Kulldorff’s spatial
scan statistical analysis using SaTScan software version 9.4.4. The scan parameters, for time range from 2006 to
2015 with time interval of one year and analysis was space-time. The maximum spatial cluster size was 25% of the
population at risk and the number of Monte Carlo simulations was restricted to 999.The likelihood ratio statistic
(LRS) of the Poisson distribution was, for each location and size of the scanning window, the alternative hypothesis
is that there is an elevated risk within the window as compared to outside. The likelihood function for a specific
window is proportional to 1
Under Hypothesis
(𝑐
𝐸[𝑐])c(
𝐶−𝑐
𝐶−𝐸[𝑐])C-c I ()
Where, C is the total number of mouth cancer cases, c is the observed number of mouth cancer cases within a
window, E[c] is the covariate adjusted expected number of cases within the window under the null hypothesis, and
C−E[c] is the expected number of cases outside the window. I() is an indicator function. When SaTScan is set to
scan only for clusters with high rates, I() is equal to 1 when the window has more cases than expected under the null
hypothesis, and 0 otherwise. The opposite is true when SaTScan is set to scan only for clusters with low rates. When
the program scan for clusters with either high or low rates then I()=1 for all windows
The statistical significance of the detected clusters was evaluated using randomization testing or Monte Carlo
hypothesis testing because the exact distribution of the LRS was unknown. Under the null hypothesis, a large
number of random datasets was generated and the LRS value for each random dataset was computed. The Monte
Carlo p-value of a window was computed as Rbeta+1R+1, where Rbeta is the number of random datasets with a LRS
higher than the LRS in the real dataset and R is the total number of random datasets. A window shows statistical
significance at α=0.05 when its LRS is higher than approximately 95% of the LRS values of the random dataset. The
windows with the most statistically significant likelihood ratios were defined as the most likely, secondary, and
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tertiary clusters, respectively. The p-values of <0.05 using 999 permutations were considered to indicate statistical
significance within the spatial clusters. Sufficient statistical power was ensured with the use of 999 replications in
the Monte Carlo simulation.
2.2.3 Purely Spatial Analysis- Discrete Poisson Model
Neighborhood variation in the incidence of mouth cancer was determined by the purely spatial scan statistic in a
discrete Poisson model. The analysis requires the incidence number of cases, population counts, and the
geographical coordinates (longitude and latitude) for each location. The standard purely spatial scan statistic
imposes a circular window (spatial cluster) on the map and it moves across the study area to compare the number of
disease cases in a geographic area (θin) with disease cases outside that area (θout). Since the results of this analysis
can be sensitive to model parameters, particularly window size, the maximum spatial cluster size is defined using the
Gini coefficient. It has been argued that the Gini coefficient is a very intuitive and systematic way to identify the
best collection and non-overlapping of clusters. The number of cases in each location was Poisson-distributed, so we
applied the exponential model-based spatial scan statistic using SaTScan.
3. Results
3.1 Joint Point Regression using ASR
In Chennai, Madras Metropolitan Tumour Registry (MMTR) registered a total of 1969 mouth cancer among men
accounting to 7.4% of the total men cancers. Mouth Cancer ranked 3rd most common cancer, (ASR: 9.4 per 100,000
population) among men in Chennai during 2006-2015. The table1 shows the joint point regression analysis of the
incidence rate shows an increasing trend with Annual Percent Change 5.26 % of mouth cancer incidence among
men and the trend was shown in Figure 1.
Table 1 - Age standardized Rate (ASR) of Mouth cancer in Chennai during 2006- 2015
Period Registered Male
Cancer Cases
Observed Mouth
Cancer
Mouth
Cancer %
ASRW Joint Point
2006 2382 135 5.7 6.2 6.29
2007 2503 145 5.8 6.5 6.62
2008 2518 191 7.6 8.6 6.97
2009 2624 148 5.6 6.5 7.34
2010 2585 195 7.5 8.2 7.72
2011 2677 173 6.5 6.9 8.13
2012 2735 203 7.4 8.0 8.56
2013 2705 233 8.6 9.1 9.01
2014 2927 268 9.2 10.2 9.48
2015 2975 278 9.3 10.3 9.98
APC0*
5.26*
ASR Age-standardized rate per 100,000, APC: Annual Percent Change
Figure 1-Trend of Mouth Cancer (ASR) in Chennai for the period 2006-2015.
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3.2 Discrete Poisson Model
Table 2 Summary of Data- Purely Spatial analysis
Study Period Number of
Locations
Total
Population
Total Mouth
cancer cases
Annual cases Per 10,000 population
2006-2015 10 2334789 1969 8.4
In this pure spatial analysis model, we infer that there is an overall 8.4 per 100,000 populations increase in annual
mouth cancer incidence among men in Chennai.
3.2.1 Purely Spatial Analysis
Table 3 Mouth cancer clusters of Low – High RR- rates using discrete Poisson model
Cluster ID/
Zone
Annual
Cases per
100000
popln.
Center
Coordinates
Radius
(km)
No. of
obs.
Cases
No. of
Exp.
Cases
Relative
Risk
P - value GINI
Cluster
LLR
#1.
Basin bridge
and
Pulianthoppu
10.4
13.130N-
80.290E
3.432
481
390
1.31
0.0000
TRUE
12.6944
# 2.
Ayanavaram
6.0
12.970N-
80.230E
0.000
168
235
0.69
0.0000
TRUE
11.9803
#3.
Kilpauk and
Saidapet
10.8
13.140N-
80.250E
3.103
371
456
0.77
0.0000
TRUE
10.8036
0.0
2.0
4.0
6.0
8.0
10.0
12.0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Trend of Mouth cancer - Men
JP0
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#4.
Nungambakk
am & Adyar
9.5
12.970N-
80.230E
7.417
393
349
1.16
0.1350
FALSE
3.2071
Table 3 depict that there were four clusters detected by using purely spatial analysis with high or low relative risk
(RR) rates using the discrete Poisson model on mouth cancer incidence among men. The most likely clusters was
observed in the location ID 1,2,3, namely Basin Bridge, Pulianthoppu , Ayanavaram, Kilpauk and Saidapet zones .
In these clusters the log likelihood ratios are #1: LLR: 12.6, #2: LLR: 11.98, #3: LLR: 10.80 with significant p-value
(p <0.000) and found as true Gini Clusters. The location ID 4 namely Nungambakkam and Adyar zones are having
low log likelihood ratio #4 LLR: 3.20 with insignificant p value (p: 0.135) and found to be False Gini cluster. In
clusters 4, it is also observed that the relative risk is 1.16 and annual increasing cases were 10 per 100,000
populations. The highest annual increasing mouth cancer incidence among men was10.8 per 1, 00,000 found in the
Kilpauk and Saidapet zones.
Table 4- Summary of Data spatial variation in Temporal Trend analysis
Study Period No.of Locations Total
Population
Total Mouth
cancer cases
Annual cases Per
10,000 population
Annual Time Trend
2006-2015 10 2334789 1969 8.4 7.505 %
In the summary table 4 it is inferred that the overall annual increase of mouth cancer among Men
is 7.505% in Chennai.
Table 5- Spatial variation in Temporal Time Trend
Cluster ID/ Zone Annual
Cases per
100000
popln.
Center
Coordin
ates
Radius
(km)
No. of
obs.
Cases
No. of
Exp.
Cases
Relative
Risk
P -
value
GINI
Cluster
LLR
#1.
Basin bridge
10.2 13.130N
-80.290E
0.000
205
170
1.229
0.0010
FALSE
9.5134
#2.
Nungambakkam
9.9 12.970N
-80.230E
0.000
143
122
1.187
0.0040
FALSE
6.5997
#3.
Tondiarpet
8.5
13.140N
-80.250E
0.000
199
198
1.0080
0.0180
FALSE
5.1617
#4.
Ice House,
Kilpauk
7.4
13.080N
-80.270E
3.247
341
388
0.8536
0.4090
FALSE
2.0980
#5.
Adyar, Saidapet
8.0
13.030N
-80.260E
5.464
408
431
0.9338
0.9810
FALSE
0.4920
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3.2.2 Spatial variation of Time Trend
Table 5- depict that there were five clusters detected by using the spatial temporal time trend analysis. In three
clusters the location ID’s #1, #2, #3 namely Basinbridge, Nungambakkam and Tondiarpet zones were statistically
significant with high log likelihood ratios. The relative risk of these clusters is also found to be high. (Cluster1 RR:
1.22, Cluster2 RR: 1.18 and Cluster3 RR: 1.00). The clusters location ID #4, #5 namely IceHouse, Kilpauk,Adyar
and Saidapet zones were statistically insignificant. The relative risk of these clusters is also found to be low.
(Cluster4 RR: 0.853, Cluster5 RR: 0.933). In this time trend analysis all locations had False Gini clusters for the
study period.
Table 6 -Linear Time Trend of the identified clusters
Clusters Obs.
Cases
Exp
Cases
LLR Inside
Trend
Outside
Trend
Inside
Linear
Outside
Linear
P Value
1 205 170 9.5134 19.5511 6.2660 0.1786 0.0608 0.0010
2 143 122 6.5997 19.5548 6.6774 0.1786 0.0646 0.0040
3 199 198 5.1617 -0.3055 8.4384 -0.0031 0.0810 0.0180
4 341 388 2.0980 3.7631 8.3035 0.0369 0.0798 0.4090
5 408 431 0.4920 5.8669 7.9480 0.0570 0.0765 0.9810
Table 6 infer that the spatial clusters of annual inside time trend is increasing 19.55% in the Basinbridge and
Nungambakkam zones and the annual increasing outside trend is 6.266%, 6.677% respectively. The other high risk
zone Tondiarpet has - 0.306% decreasing inside time trend but 8.43 % increasing outside time trend. The other
clusters ID#4 and ID#5 namely Icehouse, Kilpauk zones and Adyar, Saidapet zones were not statistically significant
but the inside time trend is increasing 3.763% followed by 5.867% and the annual outside time trend is increasing
8.30,7.948 respectively.
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Fig 2: Map of Chennai Zones
The Figure 2 visualize the spatial map of different Zones in Chennai.
Figure 3 -Clustering on spatial window
Figure - 3, depict that the result of space-time analysis with scan parameters using time (range from 2006 to 2015)
and time interval 1 year. It is found that there are two large clusters covering most of the zones.
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4. Discussion
In this study, purely spatial and the temporal trend analysis of mouth cancer among men in Chennai from 2006 to
2015 were examined using the Kulldorff’s scan statistical methods. So far, no other similar study has been done in
this area. This study explained that there was a significantly space-time clustering in distribution of mouth cancer in
Chennai zones. The high-risk areas on pure spatial clusters were mainly concentrated in the Basinbridge,
Pulianthoppu and the temporal time trend clusters were mainly concentrated in Basinbridge, Nungambakkam and
Tondiarpet areas. In this Kulldorff’ space-time scan statistical analysis to detect the spatial and space-time clusters
of mouth cancer incidence in Chennai zones, and to verify whether the geographic clusters was caused by random
variation or not. Since the zone population is varied, we used the radius of the population coverage instead of the
geographical radius. The spatial window with the maximum likelihood is defined as the most likely cluster area, and
other clusters with statistically significant log-likelihood ratios (LLR) were defined as the secondary potential
clusters. The P values of log likelihood ratio were estimated through 999 Monte Carlo simulations. The probability
value is <0.05, indicates that the incidence was significantly high risk inside of the scan window, which might be a
potential cluster of a high risk of mouth cancer in the zone. The relative risk (RR) of each cluster was calculated to
evaluate the risk of mouth cancer in the cluster areas. The selection of the maximum radius of the spatial scanning
window and the maximum length of the temporal scanning window is the most important. For this analysis the
optimal parameters were analyzed on the data of 2006-2015 using the maximum spatial cluster sizes of 25% of total
population at risk for both spatial and temporal trend analysis. space-time scanning analysis was applied to identify
the geographic areas and time periods of potential clusters with significantly higher mouth cancer incidents than that
of nearby areas.
The Kulldorff’s scan statistics take a lot of testing problems into account for evaluating geographical and temporal
distribution by using public health data. This method has been used worldwide to detect the clusters of diseases. As
is known, in the temporal and spatial model, selection of a suitable time window and spatial window was very
important for model identification. Currently, there are two methods for selecting the size of spatial window: one is
based on the geographical area, and the other method is based on the population size covered by the scanning area.
Suppose if the size of the window is large to include the low risk area, this may lead a false interpretation. The
window which covered a smaller population might be too small to detect the real high-risk area, and the high-risk
area would be separated. Thus, the high false negative rate would be an issue. In addition, several studies also
suggested choosing an appropriate window which could identify the cluster areas with less overlap.
The spatiotemporal model used in this study simultaneously considered time and space distributions. Compared with
the separated spatial scanning model and temporal scanning model, the time-space scanning makes a conclusion. In
this study using this model to detect the spatio-temporal distribution of mouth cancer in Chennai zones, from 2006
to 2015, we found that the high risk zones of Chennai. The results indicated that further prevention and cancer
control strategies should be considered in relation with the socio economical and sanitary level in the clustered
areas. This study also demonstrated the usefulness of spatial and temporal clustering analysis, disease mapping and
trend of mouth cancers in Chennai zones using SaTScan v9. 4. 4, QGIS 3.4.1 and SEER Stat software ver.4.7.0.
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5. Conclusions
In this paper, Kulldorff’s scan statistic methods were analyzed for the Pure Spatial and Spatial Temporal Trend
cluster analysis of mouth cancer incidence of men in Chennai for different zones from 2006 to 2015. It if found that
the spatial and temporal clusters were statistically significant in many zones for most of the periods. The circular
scanning widow indicates that there are high-risk areas for mouth cancer incidence which were predominantly
located in North Chennai especially in Basin bridge zone. These results suggested that it is a need to establish the
preventive and controlling strategies to be taken for the high risk zones in Chennai by the government authorities.
Acknowledgements
The author’s of this research study wish to acknowledge all Staff of Madras Metropolitan Tumour Registry
(MMTR) for their hard work and also thank the data providing centers. We also thank to the Cancer Institute (WIA)
a pioneer of cancer registry since 1981 and Indian Council of Medical Research, Govt of India.
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