DOI:http://dx.doi.org/10.7314/APJCP.2014.15.1.455
Breast Cancer Clustering in Kanagawa, Japan: A Geographic Analysis

RESEARCH ARTICLE%UHDVW&DQFHU&OXVWHULQJLQ.DQDJDZD-DSDQ$*HRJUDSKLF
Analysis
Kayoko Katayama.D]XKLWR<RNR\DPD+LURNR<DNR6XNHWRPR 1DR\XNL
Okamoto1, Toshiro Tango5<XWDND,QDEDAbstract
Background: The purpose of the present study was to determine geographic clustering of breast cancer
incidence in Kanagawa Prefecture, using cancer registry data. The study also aimed at examining the association
EHWZHHQVRFLRHFRQRPLFIDFWRUVDQGDQ\LGHQWLÀHGFOXVWHUMaterials and Methods: Incidence data were collected
IRUZRPHQZKRZHUHÀUVWGLDJQRVHGZLWKEUHDVWFDQFHUGXULQJWKHSHULRGIURP-DQXDU\WR'HFHPEHULQ.DQDJDZD7KHGDWDFRQVLVWHGRILQFLGHQFHFDVHVH[WUDFWHGIURPWKHWRWDORI.DQDJDZD&DQFHU5HJLVWUDWLRQGDWDLVVXHGLQ7RDGMXVWIRUGLIIHUHQFHVLQDJHGLVWULEXWLRQWKHVWDQGDUGL]HGPRUWDOLW\UDWLR
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(e.g., city, special ward, town, and village) in Kanagawa by an indirect method using Kanagawa female population
data. Spatial scan statistics were used to detect any area of elevated risk as a cluster for breast cancer deaths and/
RULQFLGHQFHV7KH6WXGHQWWWHVWZDVSHUIRUPHGWRH[DPLQHGLIIHUHQFHVLQVRFLRHFRQRPLFYDULDEOHVYL]SHUVRQVSHUKRXVHKROGWRWDOIHUWLOLW\UDWHDJHDWÀUVWPDUULDJHIRUZRPHQDQGPDUULDJHUDWHEHWZHHQFOXVWHUDQGRWKHU
regions. Results:7KHUHZDVDVWDWLVWLFDOO\VLJQLÀFDQWFOXVWHURIEUHDVWFDQFHULQFLGHQFH S  FRPSRVHGRIPXQLFLSDOLWLHVLQVRXWKHDVWHUQDUHDRI.DQDJDZD3UHIHFWXUHZKRVH6,5ZDVSHUFHQWKLJKHUWKDQWKDWRI
WKHUHPDLQGHURI.DQDJDZD3UHIHFWXUH,QWKLVFOXVWHUDYHUDJHYDOXHRIDJHDWÀUVWPDUULDJHIRUZRPHQZDVVLJQLÀFDQWO\KLJKHUWKDQLQWKHUHVWRI.DQDJDZD S  1RVWDWLVWLFDOO\VLJQLÀFDQWFOXVWHUVRIEUHDVWFDQFHUGHDWKVZHUHGHWHFWHG S  &RQFOXVLRQV7KHUHZDVDVWDWLVWLFDOO\VLJQLÀFDQWFOXVWHURIKLJKEUHDVWFDQFHU
incidence in southeastern area of Kanagawa Prefecture. It was suggested that the cluster region was related tothe tendency to marry later. This study methodology will be helpful in the analysis of geographical disparities
in cancer deaths and incidence.
Keywords: Breast cancer - cancer registry data - regional clustering - spatial epidemiology
Asian Pac J Cancer Prev, 15 (1), 455-460

Introduction
In the past, cancer control and prevention in Japan
have been organized according to 47 Prefectures.
Therefore, cancer data are often evaluated at the levelof the prefectural unit. It has long been known that there
is a regional disparity in cancer mortality (Kuroiwa etal., 2004). Such regional disparities were shown to beprominent in gastric, liver, breast cancer, and leukemia
and age-adjusted cancer mortality under age 75 years
old (Sobue et al., 2012). Among women in Japan, breast
cancer mortality is higher in metropolitan regions such as
Tokyo, as well as in Kanagawa and Saitama Prefecturesin eastern Japan. Detailed observations have indicated asecular change in regional disparities in mortality (Cancer
Statistics Editorial Committee, 2010); these reportssuggest that breast cancer is sometimes concentrated inD VSHFLÀF UHJLRQ +RZHYHU FDQFHU GDWD UHÁHFWLQJ DQ\differences that may exist between the urban regions, rural
UHJLRQVDQGÀVKLQJYLOODJHVRIWKHVDPHSUHIHFWXUHPD\
be ‘averaged out’ when the data are evaluated at the levelof the prefectural unit. Such averaging could result in a
failure to clearly depict real regional disparities. (Menget al., 2005).
In light of these circumstances, attempts have been
made to monitor cancer-related data on the basis of smaller
region units, e.g., counties in the United States (Hoover
et al., 1975); and cities, counties, and regional meshes inJapan (Ookubo et al., 1977; Fukuda et al., 2005; Nakaya,
2011). Monitoring on the basis of smaller region units
enables more detailed examinations of the incidences of
diseases, as well as mortalities, and enables more accurate
LGHQWLÀFDWLRQRIWKHUHJLRQVWKDWUHTXLUHLQFUHDVHGHIIRUWV
regarding prevention, including early detection and early1Cancer Prevention and Cancer Control Division, Kanagawa Cancer Center Research Institute, 2Department of Epidemiology andEnvironmental Health, Juntendo University Faculty of Medicine, 4Center for Cancer Control and information Services, National
Cancer Center, 3Japan Women’s College of Physical Education, Faculty of Sports and Health Sciences, 5Center for Medical Statistics,
Tokyo, Japan *For correspondence: suketomo.hiroko@jwcpe.ac.jp$VLDQ3DFLÀF-RXUQDORI&DQFHU3UHYHQWLRQ9RO

455

Kayoko Katayama et al

treatment. To formulate effective measures for cancer
prevention and control, especially regarding cancers withhigh survival rates, such as breast cancer, both mortality
and incidence data need to be taken into consideration
when determining the optimal focus for such measures.
It would therefore be useful to clarify the mapwith geographical distribution of cancer incidence andmortality. In recent years, increasing attention has been
JLYHQ WR DQ DFDGHPLF ÀHOG FDOOHG VSDWLDO HSLGHPLRORJ\(Kulldorff. 1997). This is an academic discipline that aimsi) to describe geographical variations in the occurrence ofsymptoms representing health risks, diseases, and deaths,ii) to consider geographical variations in risk factors suchas demographical, environmental, and socio-economic
status (SES), iii) to detect systematic and non-randomchanges and differences in geographical factors underlying
diseases, and iv) to carry out factor analysis (Tango et al.,2007).
One tool that has been effectively used for this purposeis the disease map, which can take the form of a “point
data map” or a “counting data map”. Counting data maps
usually display either relative rates in each region, as
measured by a standardized mortality ratio (SMR) or ageadjusted mortality rate or some similar index. In addition,
increasing attention has been paid to spatial epidemiology,
largely due to the establishment of statistical methods foranalyzing spatial information relevant to diseases and dueto the availability of geographical information concerning
risk factors (Tango et al., 2007).
Previous studies using geographic analysis havedemonstrated regional clustering of breast cancer only formortality data. (Kulldorff et al., 1997, Doi et al., 2010).
The purpose of this study is to investigate whether there
are clusters or breast cancer deaths or clusters of breast
FDQFHULQFLGHQFHRIVWDWLVWLFDOVLJQLÀFDQFHLQ.DQDJDZD
Prefecture. SMR or SIR values are unstable in regions withparticularly small populations, rendering them unsuited forregional comparison with other municipalities of different
population sizes (Tango, 2000). In this study attempted
LGHQWLÀHGKLJKPRUWDOLW\DQGLQFLGHQFHUHJLRQRIEUHDVW
cancer using spatial scan method.
The study also aimed at examining the association
EHWZHHQVRFLRHFRQRPLFIDFWRUVDQGLGHQWLÀHGFOXVWHULQJ
regions.

Materials and Methods
Data collection
Kanagawa Prefecture, Japan is located in the southwestof the Kanto region and south of Tokyo (Figure 1). In 2010,
the population of Kanagawa Prefecture was 9,048,331
people, second only to Tokyo. To compile incidence data
abstract from the Kanagawa population-based cancer
registry, we used data for women diagnosed with breast
FDQFHUIRUWKHÀUVWWLPHIURP-DQXDU\WR'HFHPEHU
The data consisted of 2,326 incidences were extracted
from a total of 34,323 incidences, including gender,
age and residential address (Kanagawa Prefecture,
Health and Welfare Division 2011). We also extracted
the number of observed deaths due to breast cancerfrom data regarding 794 deaths, including gender, age,



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Figure 1. .DQDJDZD3UHIHFWXUHLQ-DSDQ
DQGVLPSOLÀHGFODVVLÀFDWLRQRIFDXVHRIGHDWKIURPWKH
Population Survey Report statistical archives chapter eachcity, district, town, and village in Kanagawa Prefecture(Statistical Yearbook of Kanagawa Prefectural Public
Health, 2006).
Population figures were based on Kanagawa
Prefecture’s women population (Ministry of Internal
Affairs and Communications, Bureau of Statistics,
Population census 2005). Subjects were divided into
three age groups, viz 0–14, 15–64, and 65 years and over.
To obtain SMR for each age group, expected mortality
was calculated by multiplying the determined mortality
rates by the populations of the corresponding groups ineach of 56 municipalities (e.g., city, town, or village)
respectively. SMR was then expressed as a ratio of the
expected mortality to the corresponding number ofdeaths encountered in each municipality. Similarly, SIR
was calculated from the incidence data from the regional
cancer registry (Table 1). All the analyses were based on
56 municipalities (e.g., cities, special wards, towns, andvillages) in Kanagawa Prefecture.
SMR and SIR were then plotted on the map for eachmunicipality in Kanagawa Prefecture. To draw this map,
latitude and longitude data of municipalities of Kanagawa
Prefecture were collected from the location information
handbook for municipalities issued by the Japan Map
Center of the Geographical Survey Institute of Japan(Japan Map Center, 2009). The points used as coordinates
IRUPXQLFLSDOLWLHVZHUHWKHORFDWLRQVRIWKHRIÀFHVRIWKH
respective municipal governmental bodies using Mapinfo
Professional version 11.0 (Pitney Bowes Software K.K).
The variables of persons per household, total fertility
UDWHDQGDJHDWÀUVWPDUULDJHIRUZRPHQDQGPDUULDJH
rate were taken from the vital statistics (Kanagawa
Prefecture, Bureau of Health Welfare 2006).
Statistical analysis
A disease cluster is defined as a geographically
ERXQGHG JURXS RI RFFXUUHQFHV RI VXIÀFLHQW VL]H DQGconcentration to be unlikely to have occurred by chance
without any assumptions about the shape or form of
the cluster. To identify the disease clusters and their
DSSUR[LPDWH ORFDWLRQ ZH XVHG D ÁH[LEOH VSDWLDO VFDQ
statistic with are restricted likelihood ratio (Tango andDOI:http://dx.doi.org/10.7314/APJCP.2014.15.1.455
Breast Cancer Clustering in Kanagawa, Japan: A Geographic Analysis

7DEOH%UHDVW&DQFHU605DQG6,5LQ.DQDJDZD3UHIHFWXUH  DQG3RSXODWLRQ'HQVLW\WRWDO)HUWLOLW\
UDWHSHUVRQVSHU+RXVHKROGVÀUVW0DUULDJH$JHRI:RPHQ0DUULDJHUDWHDQG0RUWDOLW\Population
Persons
density*perperson/km² households

Total
First
fertility marriage age
rate
of women

Marriage Mortality
rate(deathnumber)

1.54
1.91
2.52
2.47
2.96
2.40
2.94
2.89
1.59
1.70
2.83
2.55
1.86
2.82
2.96
2.67
1.52
1.30
1.09
1.06
1.73
1.15
1.28
1.23
1.30
2.67
2.61
1.62
2.28
1.19
2.21
3.03
1.81
2.01
1.17
2.54
2.61
1.69
2.35
2.49
1.12
2.79
3.88
2.43
2.24
1.28
1.54
1.32
1.17
1.78
1.28
1.48
2.80
0.79
3.48
4.48
2.08
0.79
1.96

7,991
9,249
12,1606,780
15,56011,125
100.0
9,329
7,563
8,482
6,852
10,115
75.0
6,756
8,490
6,547
7,480
50.0
6,729
6,493
7,449
5,245
25.0
14,418
14,546
12,540
11,243
10,061
0
6,758
4,203
3,831
4,364
5,733
1,744
6,409
3,368
2,745
1,538
1,627
2,386
8,218
1,812
4,703
7,263
574
3,670
1,859
3,536
1,901
3,301
506
1,227
324
55
2,361
151
1,234
665
1,225
50
5,581
4,103
5,489

1.30
1.08
1.01
1.05
1.03
1.06
1.18
1.20
1.16
1.34
1.18
1.18
1.32
1.34
1.23
1.29
1.20
1.33
1.32
1.32
1.12
1.21
1.10
1.24
1.17
1.26
1.29
1.10
1.21
1.21
1.20
1.14
1.17
1.08
1.12
1.24
1.24
1.25
1.23
1.28
1.31
1.27
1.06
1.32
1.07
1.04
1.06
1.62
0.93
0.90
1.64
1.06
1.19
1.25
1.17
0.98
1.19
0.14
1.20

7.8
7.4
8.0
7.7
6.6
5.4
6.0
5.0
8.2
6.4
5.4
5.4
6.3
6.1
5.5
5.5
6.2
5.7
7.8
7.7
10.2
8.6
8.3
7.4
5.9
5.5
5.9
5.6
6.2
5.8
6.0
4.9
6.2
3.9
5.1
6.3
7.7
6.6
6.5
6.5
5.6
6.1
4.5
5.9
4.6
4.2
4.1
5.0
4.3
3.7
8.0
5.4
3.9
4.4
5.3
3.4
6.03
1.40
5.93

2.25
2.11
2.01
1.97
2.20
2.56
2.37
2.56
2.37
2.49
2.19
2.55
2.58
2.71
2.56
2.55
2.72
2.62
2.14
2.26
2.03
2.19
2.44
2.10
2.44
2.62
2.56
2.45
2.43
2.63
2.56
2.49
2.43
2.78
2.52
2.46
2.40
2.50
2.61
2.46
2.89
2.70
2.64
2.73
2.73
2.70
3.10
2.92
2.71
3.13
2.94
2.05
2.61
2.52
2.76
3.03
2.52
0.27
2.55

28.2
28.2
28.6
28.4
28.2
28.1
28.1
28.1
28.7
28.1
28.2
27.9
28.1
27.9
28.0
28.1
28.4
28.3
27.6
28.3
28.9
28.6
28.9
28.3
28.7
27.5
27.5
28.7
28.1
27.7
28.0
29.2
27.7
26.8
27.3
27.4
28.0
27.7
28.1
27.7
27.1
26.9
27.8
27.2
28.5
28.6
27.6
26.3
26.6
27.3
27.1
26.8
26.4
26.8
25.8
28.3
27.84
0.72
28.02

28
21
6
20
19
16
18 100.0
19
25
24
26
75.0
23
13
12
15
50.0
20
17
11
28
18
25.0
16
9
19
16
0
13
47
21
18
33
14
23
4
54
12
10
18
24
10
10
3
6
5
6
4
7
3
0
0
0
0
2
2
0
2
3
1
14.18
11.25
13.50

None

1.20
1.10
0.80
1.60
1.00
0.90
1.20
1.00
0.90
1.00
1.30
1.00
0.90
1.00
1.30
1.50
0.70
0.80
Kawasaki
1.60
1.40
0.90
0.50
1.10
0.90
0.90
Yokosuka
1.20
Hiratsuka
0.90
Kamakura
1.00
Hujisawa
0.90
Odawara0.70
Chigasaki
1.10
Zushi
0.70
Sagamihara
0.90
Miura
2.40
Hadano0.70
Atsugi
1.00
Yamato1.30
Isehara
1.20
Ebina
1.00
Zama
0.30
Minami-ashigara
1.50
Ayase
0.70
Miura-county
Hayama1.80
Kouza-county
Samukawa
1.00
Naka-county
Oiso
2.10
Ninomiya
1.00
Asigara-county
Nakai
0.00
Asigarakami-county
Oi0.00
Matsuda0.00
Yamakita
0.00
Kaisei
1.40
Asigarashimo-county
Hakone1.30
Manazuru
0.00
Yugawara
0.60
Aiko-county
Aikawa0.90
Kiyokawa-mura 3.00
Average
1.02
Standard deviation
0.55
Median1.00

SIR**

Remission

SMR**

Persistence or recurrence

Tsurumi-ku
Kanagawa-ku
Nishi-Ku
Naka-Ku
Minami-Ku
Hodogaya-Ku
Isogo-Ku
Kanazawa-Ku
Kouhoku-Ku
Totsuka-Ku
Kounan-Ku
Asahi-Ku
Midori-Ku
Seya-Ku
Sakae-Ku
Izumi-Ku
Aoba-Ku
Tuzuki-Ku
Kawasaki-Ku
Saiwai-Ku
Nakahara-Ku
Takatsu-Ku
Tama-Ku
Miyamae-Ku
Asao-Ku

Town

Newly diagnosed with treatment

Yokohama

Ward

Newly diagnosed without treatment

City (County)*Standard population is 2005 data, **We calculated Kanagawa female’s population for standard population (2005), Ascending order to JIS geographical name code(
local government code)

Takahashi, 2005, 2012; Tango, 2008). We assumed thenumber of deaths or incidence to be Poisson distributedin each municipality. This method can detect clusters ofany size and form located anywhere in the study region,
whether or not they cross administrative borders.
 7KHPRVWOLNHO\FOXVWHUZDVGHÀQHGDVWKDWZLWKWKH

PD[LPXP OLNHOLKRRG UDWLR 6WDWLVWLFDO VLJQLÀFDQFH RI
clustering was based on Monte Carlo hypothesis testing(Tango, 2007), comparing the likelihood ratio test statisticfrom the observed data set with the test statistic from 999
random data sets generated under the null hypothesis of
no clustering. The calculations were performed using the$VLDQ3DFLÀF-RXUQDORI&DQFHU3UHYHQWLRQ9RO

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Kayoko Katayama et al

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Statistic
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2EVHUYHGIUHTXHQF\ ([SHFWHGIUHTXHQF\Mortality :Tsurumi-ku, Kawasaki-ku, Saiwai-ku (3 regions)74
54.17
Incidence: Nishi-ku, Naka-ku, Minami-ku, Hodogaya-ku,
1667
1236.42
Isogo-ku, Kanazawa-ku, Kounan-ku, Sakae-ku, Yokosuka-city, Zushi-city, Hayama-twon (11regions)

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1.35

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
   )LJXUHBreast cancer SMR and SIR in Kanagawa
above SMR, below SIR
program FlexScan version 3.1 developed by Takahashi etDO  GHVLJQHGVSHFLÀFDOO\WRLPSOHPHQWVSDWLDOVFDQ
statistics.
To characterize a detected spatial cluster, we estimatedthe relative risk, observed number of cases divided by the
expected number of cases within the clusters, which arealso called SIR or SMR.
Student’s t-test was performed to examine differencesin socioeconomic variables, viz, persons per household,
WRWDOIHUWLOLW\UDWHDJHDWÀUVWPDUULDJHIRUZRPHQDQG
marriage rate, between the most likely cluster and otherregions were evaluated using SPSS version 19 (IBM SPSS
Statistics).

Results
Table 1 shows population density, SMR, and SIR among
women in Kanagawa Prefecture. Persons per household,
WRWDOIHUWLOLW\UDWHDJHDWÀUVWPDUULDJHIRUZRPHQDQG
marriage rate were presented as reference indicators.
Figure. 2 shows the distribution of SMR and SIR for breast
cancer in Kanagawa. Three municipalities had particularlyhigh SMR, viz Kiyokawa Village of Aiko County, Oiso

458

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Dark color are most likely clusters, respectively [above; deaths
ZDVQRWVWDWLVWLFDOO\VLJQLÀFDQW S  EHORZLQFLGHQFHZDVVWDWLVWLFDOO\VLJQLÀFDQW S  @7DEOH'LIIHUQFHLQ6RFLDO(FRQRPLF6WDWXVEHWZHHQ
WKH0RVW/LNHO\&OXVWHURI,QFLGHQFHDQGRWKHU5HJLRQV
Persons per household
Total fertility rate
First-marriage age of women
Marriage rate

Most likely cluster

Other regions

Pa

2.78±0.25(person)
1.13±0.09(person)28.2±0.43(years old)
5.86±1.13(percent)2.55±0.27
1.210±0.14
27.8±0.75
6.07±1.47

0.055
0.073
0.017
0.667

a

; Student t-test

Town of Naka County and Miura City. Four municipalities
had particularly high SIR, viz Zushi City, Hayama Town,
Kiyokawa Village of Aiko County, and Aikawa Town of$LNR&RXQW\ )LJXUH 7KHÁH[LEOHVSDWLDOVFDQVWDWLVWLF
found the most likely cluster of breast cancer deaths in
the area composed of three municipalities, Tsurumi-ku,
Kawasaki-ku, and Saiwai-ku (Figure. 3), but it was not
VWDWLVWLFDOO\VLJLQLÀFDQW 7DEOH 7KHPRVWOLNHO\FOXVWHU
of incidence in the area composed of 11 municipalities,(Nishi-ku, Naka-ku, Minami-ku, Hodogaya-ku, Isogo-ku,
Kanazawa-ku, Konan-ku, and Sakae-ku of Yokohama
City, as well as Yokosuka City, Zushi City, and Hayama

DOI:http://dx.doi.org/10.7314/APJCP.2014.15.1.455
Breast Cancer Clustering in Kanagawa, Japan: A Geographic Analysis

7RZQ DUHVKRZQLQ)LJ,WZDVVWDWLVWLFDOO\VLJLQLÀFDQW(SIR=1.35, p=0.001). The method found the most likely
cluster in a region encompassing the southeastern area in
Kanagawa Prefecture, which a incidence rate 35 percent
higher than tha rest of 45 municipalities. The most likely
cluster was in the southeastern Kanagawa which were nothigh SIR four municipalities.
 ,Q WKH PRVW OLNHO\ FOXVWHU DJH DW ÀUVWPDUULDJH IRUZRPHQZDV\HDUVROGZKLFKZDVVLJQLÀFDQWO\higher than other regions (p=0.017), whereas, total fertility
rate, persons per household and marriage rate were not
VLJQLÀFDQWL\GLIIHUHQW 7DEOH 'LVFXVVLRQ
Maps based on SMR and SIR by administrative
division can visually show the geographical distributionof health affairs. Recently, the clustering method in spatial
epidemiology has enabled statistical detection of theexistence of clusters. The present study aimed to detect
the presence or absence of clusters, viz, high mortality or
incidence rate area through the use of breast cancer data
for each city, town, and village in Kanagawa Prefecture.
Although the use of the maps based on SMR or SIR
has been longstanding and widespread, SMR and SIR
have also been discussed as problematic with respect totheir functions as indicators (Tango, 2000). The biggest
problems with these metrics are that SMR and SIR are
affected by the population size of the subject region, andthat SMR and SIR values are unstable in regions withparticularly small populations, rendering them unsuited forregional comparison with other municipalities of different
population sizes (Tango, 2000).
In this study, cluster detection test for breast cancer
mortality in Kanagawa Prefecture demonstrated noclustering of mortality; regions with high SMR were
not simply clusters of mortality from breast cancer. The
disease maps which based on SIR and SMR revealed
different distributions. Undeniably, observation is only theÀUVWVWHSRIWKHDQDO\VLVEXWDQDQDO\VLVPHWKRGLVQHHGHG
to statistically determine whether disease clustering ispresent.
We used a spatial epidemiological clustering method
to detect a cluster of breast cancer incidence in thesoutheast of Kanagawa Prefecture, and thus demonstrated
the application of the method as a cluster detection test.
Several prior studies have considered cluster detection test.
One such study used data from 1978-1982 regarding 592
cases of leukemia and the population data for each of the790 national census tracts in a region consisting of eight
counties of New York in the United States (Alexander
et al., 1992). This study is a widely known case of test
method application proposed for the testing for spatial
clustering (Openshaw et al., 1988; Waller et al., 1992).
The northeastern United States is known for its highmortality due to breast cancer. Kulldorff et al (1997)
conducted an analysis to determine whether regionswith high breast cancer mortality are evenly distributed
across the northeastern United States, or whether breast
FDQFHULVLQVWHDGFOXVWHUHGLQVSHFLÀFUHJLRQV7RWKLVHQG
they examined population data and mortality rate by age

group from 1988-1992 for 244 counties of 11 states andthe District of Colombia, a total of 245 counties. TheirDQDO\VHV LGHQWLÀHG PRVW OLNHO\ FOXVWHUV LQGLFDWLQJ WKDWthe high breast cancer mortality in some regions was
not due to accidental error; this result held true evenafter adjustments were made to accommodate presumed
confounding variables. SaTScan (circular scan method)of these previous studies is simple, the issue has beenraised that only clusters of regions in circular shapes can
EHLGHQWLÀHG 7DQJRHWDO 2QWKHRWKHUKDQGZLWKWKHÁH[LEOHVFDQPHWKRGDOVRQRQFLUFXODUFOXVWHUVFDQEH
LGHQWLÀHGUHQGHULQJFRPSOH[VKDSHVFOHDUO\LGHQWLÀDEOH
that are otherwise not accessible.
In Japan, one out of every two persons is eventuallydiagnosed with some form of cancer (Matsuda et al.,2011). Because cancer is a disease of national importance,
countermeasures are urgently needed. Particularly forbreast cancer and other cancer types with relatively
good prognoses and high survival rates, considerationof mortality data alone will not lead to effective
FRXQWHUPHDVXUHV,Q.DQDJDZD3UHIHFWXUHWKHÀYH\HDU
relative survival rate for breast cancer is high: 89.2 %(2005).
The geographical distributions of mortality andincidence point to different regions, which are apparent
even upon visual inspection. In the incidence of breast
cancer, it is critically important to identify the risk factors
for cancer incidence and using the results of these analyses
to inform prevention strategies. Several epidemiologicalstudies have shown a connection between breast cancer
risk and having received female hormone or experienced
childbirth; the number of births also makes a contribution(Kelsey, 1993; Ewertz and Duffy, 1988; Fukuda et al, 7KH LGHQWLÀHG PRVW OLNHO\ FOXVWHU UHJLRQ RI WKLV
paper showed that women are getting married at later ages
than other regions, and the connection with the precedence
studies were suggested.
A limitation of our present study is that there are
no data available at the smaller region level regarding
the percentages of women who undergo breast cancer
screening. Therefore, we referenced the data on breast
cancer screening for all of Yokohama City as an indicator;
very low screening rates were obtained: 9.5% in 2007,
11.3% in 2008, 14.5% in 2009, and 12.9% in 2010. The
fact that clustering of incidence was observed in spiteof the low screening rate implies that many cases are
not detected in the early stages. Proactive measures arenecessary to accelerate improvement of screening rates
among the women living in regions showing clusteringof breast cancer incidences.
In the population-based cancer registry of Kanagawa
Prefecture for the year 2006, used in this study, the deathFHUWLÀFDWHRQO\ '&2 UDWHZDV'&2LVDPHDVXUH
of the accuracy of cancer registration, and the lower the
DCO rate, the better. The legal framework for populationbased cancer-registry systems had not been established thepast in Japan. In 2012, population-based cancer registries
were initiated in all 47 prefectures of Japan, including theTokyo metropolitan, and Cancer Registry Act was enacted
LQ'HFHPEHUÀQDOO\Therefore, it is expected that accuracy will rapidly$VLDQ3DFLÀF-RXUQDORI&DQFHU3UHYHQWLRQ9RO

459

Kayoko Katayama et al

improve in the future. Enhanced accuracy of cancerregistry data across the country will increase theprobability of successfully promoting effective cancer
countermeasures that are grounded in science. Research
regarding the effective use of cancer registry data should
be conducted in such a way that it is widely acknowledgedby the general public. It is one method to visualize thetrend of cancer using maps.
In conclusion, we reported on smaller regions in
Kanagawa Prefecture that exhibit clustering of breast
cancer. This study methodology will be helpful in theanalysis of geographical disparities in cancer mortality
and incidence revealed in cancer registry data, for the
purpose of eliminating regional disparities in cancercare. These maps should be regarded as tools for forming
hypotheses leading to the next step of the study, for
H[DPSOH DWWHPSWLQJ WR LGHQWLI\ IDFWRUV XQLTXH WR WKHUHJLRQVZKHUHFOXVWHULQJKDVEHHQGHWHFWHGRULGHQWLÀHGTo establish cancer control and prevention measures in
the future, it will be important to perform comparisons
between smaller regions in each prefecture of Japan, andfurther research is needed to elucidate the contributing
factors in clustering of breast cancer incidence.

Acknowledgements
The authors and the research team would like to thankthe staff members and the registrars at the Kanagawa
Cancer Center, the population-based cancer registry
Office, who have been involved with the Kanagawa
Cancer Registry. The authors thank professor Satoshi
Kaneko of Nagasaki University for his relevant advice
and encouragement. This work was supported by JSPS
KAKENHI Grant Number 23659365.

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