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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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Volome 8, Issue 12, December 2019

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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