社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

原著

Prefectural mortality in relationship to socioeconomic status andlong-term care in Japan

日本における都道府県別に見た死亡率と要介護状況と社会経済状況との関連
Suwen Yang1）, Tanji Hoshi2）楊素雯 1）、星旦二 2）1）Institute of Population Research, Shandong Academy of Social Sciences, China
2）Graduate School of Urban Environmental Science, Tokyo Metropolitan University, Japan1）中国

山東省社会科学院人口学研究所

2）首都大学東京

都市環境科学研究科

Abstract
Objectives: The purposes of this study were to explore the distribution of mortality, long-term care (LTC) andsocioeconomic status (SES) among prefectures in Japan, and to elucidate the association between SES, LTC, andmortality indictors by using aggregated panel data. Methods: Prefectures in Japan were used as units of analysis
and 7 indicators were obtained from multiple data sources, which were published by government organizations
and public institutions for years centering on 1995, 2000, and 2005. The quintile distributions of the main variables
are presented using the Map Win program. Data analyses were performed using the statistical package SPSS
19.0 for Windows. Results: As a result of correlation analysis, a decreased mortality was found to be associatedwith higher percentage of SES and lower rate of certification for LTC. With respect to gender differences,male mortality was predicted by SES and LTC more strongly than female mortality. According to the linear
regression analysis, the effect of SES on the death rate of elderly became stronger and more significant in 2005.
Conclusions: In conclusion, the factors that explain the mortality, LTC, and SES variations in Japan demonstrated
that prefectures that had higher socioeconomic levels and lower LTC application rates had lower death rates.
Future studies with individual-level analyses should be conducted to provide more conclusive evidence.
目的：本研究の目的は、日本の 47 都道府県別で見た死亡率、要介護認定割合そして社会経済的要因との関連を明確にすることである。
研究方法：日本国が提示した、1995、2000 と 2005 年の情報を活用し、Map Windows と SPSS 19.0 で解析した。
結果：経年的に見た死亡率の低減化と要介護割合の低減化は、社会経済的要因と統計学的にみて有意に関連し、女
性よりも男性の方が強く関連していた。社会経済的要因が優れ要介護割合が低い地域では、総死亡率が少ない傾向を示した。
Key words：mortality, socioeconomic status, Long-term care
キーワード：死亡率、社会経済学的状態、要介護状況― 159 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

1．Introductionthat people with a higher income might find it easier

Populations are aging and life expectancy

to pay for LTC, and might therefore, ceteris paribus,

continues to increase throughout most of the world

be more inclined to enter residential care. However,

due to the profound changes in the prevailinga higher income might also facilitate access to homepatterns of disease and morbidity. As one of thecare services and might therefore assist in delaying

fastest aging developed countries, Japan has theresidential care entry 7） . In 2000, the Japanese

longest life expectancy and healthy life expectancy

government introduced LTC insurance for older(HALE) at birth in the world. These achievements

people requiring nursing care, and citizens age

in the Japanese population, particularly in the 1960s

years can receive insurance benefits after applicationand 1970s, are suggested to be based not only on

and certification that they require such care.

improvement in the standards of living due to

Although the costs of LTC are covered by public

economic growth but also on a relatively smaller

programs for people regardless of their income level,1）, 2）. Nonetheless, significant

few studies have been conducted on the association

geographical variation in health levels is found evenbetween SES and the LTC application rate. Since

in Japan. According to the prefectural estimates

LTC insurance unions are managed by municipalities

published by the Ministry of Health, Labour andin Japan, the LTC data require an application rate so

Welfare (2012), HALE at birth by prefecture wasthat they can be collected at the prefectural level.

socioeconomic disparity

40

found to vary from 68.95 (Aomori Prefecture) to 71.74

The study of the socioeconomic factors that

(Aichi Prefecture) for males and from 72.37 (Shiga

determine prefectural mortality variations in Japan is

Prefecture) to 75.32 (Shizuoka Prefecture) for females

a matter of great interest, because it helps to provide

in 2010.

rational guidance on possible activities to reduce

In Japan, systematic studies using municipal datasuch differences and, in short, to increase the level ofregarding all causes and cause-specific mortality

health in the whole population. The purposes of thisalong with several socioeconomic indicators

study were to explore the distribution of mortality,

showed significant relationships between regional

LTC, and SES among prefectures in Japan from 19953）, 4）, 5）.

to 2005, and to elucidate the association between

Socioeconomic status (SES) reflects different aspects

them by using aggregated panel data. Finally, timeof social stratification, and people with low SES

trends and gender differences were explored.

mortality and socioeconomic characteristics

have a higher mortality than those with better SES.
Because no SES index has yet been established for2．Methods

research in Japan, this study used the traditional SES

2.1

Study Unites

indicators for analysis, including income, education,

According to Local Autonomy Law, local public

and occupation at the prefectural level. A small

entities in Japan are divided into two categories. The

but growing body of literature on socioeconomic

prefectures of Japan are the country’s 47 first-orderinequality in morbidity among older individuals

subnational jurisdictions on a state or provincial level:

suggests that social inequality in health persists into

1“metropolis”(in Japanese,“to”), Tokyo; 1“circuit/old age. Moreover, frailty among elderly individuals is

territory”“do”(), Hokkaido; 2 urban prefecturesassociated with their SES and is strongly associated“fu”(), Osaka and Kyoto; and 43 other prefectureswith their health- and home-care utilization . SES“ken”(). There are currently 47 prefectures, increased

indicators such as education, income, wealth, andfrom 46 in 1972 with the reversion of the Okinawa

homeownership are also predictors of long-term care

Prefectures to Japan. Another category consists of(LTC) use; however, the evidence for each indicator

cities, towns, and villages “shi,”(“cho,”and“son”).

6）

being a predictor of LTC is inconclusive. For

The 47 prefectures in Japan were used as units ofexample, the income effect refers to the possibility

analysis, which are grouped into nine regions based― 160 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

on their geographic divisions: Hokkaido, Tohoku,

annual taxable income by the total prefectural

Kanto, Chubu, Kansai, Chugoku, Shikoku, Kyushu,

population from National Accounts of Japan,

and Okinawa.

Cabinet Office. Total employment rates reflected
the percentage of employed people age 15―65 years

2.2

Data collectionin the total workforce, which were collected from

This study adopts a prefecture-based data analysis,
considering the influence of SES on mortality andPopulation Census, Ministry of Internal Affairs andCommunication.

LTC. We selected seven indicators obtained frommultiple data sources, which were published by

2.3

government organizations and public institutions

Statistical analysis was conducted separately

for specific years: 1995, 2000, and 2005. Everyfor each of the dependent variables for each year.

conceivable variable of interest was considered.

First, basic descriptive statistics were calculated.

Mortality as a dependent variable was explained

Statistical methods

For descriptive observations and screening purpose,by a series of indicators in this study: age-adjusted

variations of the selected variables in different years

death rate (per 1,000 people) for males, age-adjusted

were measured, including the mean, minimum,

death rate (per 1,000 people) for females, and death

maximum, standard deviation (SD), and coefficient ofrate for people

65 years (per 1,000 people). As

variance (CV). Then, Pearson’s correlation coefficientsthe age distributions were different over the study

between mortality, LTC, and the composite

period, age-adjusted death rates by gender wereindices of socioeconomic factors were calculated.

calculated using Population Censuses data fromThird, variables showing a statistically significant

Ministry of Internal Affairs and Communication inassociation with dependent variables in different

1995, 2000, and 2005; however, the death rates ofyears were used in linear regression analysis. The

peopleregression model described the linear association

65 years old were available only for 2000

between a single SES indicator, LTC applicationand 2005.
Rates of certification for LTC need wererate, and mortality. Considering the multicollinearity

calculated by dividing the number of applications

effects among the three independent variables,

for certification of need for LTC (or support) by the

only the simple (univariate) regression analysis

number of insured people age

65 years. Data were

was applied. Various tests were then employed

not available in 1995, because LTC insurance was not

to determine whether the model is satisfactory. If

introduced until April 2000. Data were collected fromthe model is deemed satisfactory, the estimatedthe Report on Long-term Care Insurance Operation,

regression equation can be used to predict a value of

Ministry of Health, Labour and Welfare in 2000 andeach dependent variable given known values of the2005.

independent variable.

Three prefectural SES indicators were used as

The quintile distributions of the main variables are

independent variables: enrollment rates in higher

presented using the Map Win program. All variables

education (%), per capita income (1,000 yen), andused contained no missing data. All p values aretotal employment rate (%). Data for all indicators

two-tailed. Data analyses were performed using thewere obtained in 1995, 2000, and 2005. Enrollment

statistical package SPSS 19.0 for Windows.

rates in higher education referred to the percentage
of upper secondary school graduates that moved

3．Results

on to higher education, which were collected

3.1

Descriptive characteristics

from School Basic Survey, Ministry of Education,

The mortality, SES, and LTC characteristics

Culture, Sports, Science and Technology. Per capita

of the 47 prefectures in 1995, 2000, and 2005 areincome was calculated by dividing the aggregatedsummarized in Table 1. From 1995 to 2005, the― 161 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

average levels (mean) of three variables were foundto continuously increase: death rate of individuals
65 years old, rate of certification for LTC need, andenrollment rate in higher education; in contrast, four
variables were found to continuously decrease: ageadjusted death rate of both genders, prefectural per
capita income, and total employment rate. Large
geographic variations in select variables wereobserved in each year. For example, the age-adjusted
death rate of the 47 prefectures in 2005 ranged from5.39 to 7.33 for men, 2.71 to 3.24 for women, and 31.18
to 38.22 for individuals age

Figure 2

65 years.

Distributions of prefectural per capita income inJapan in 1995, 2000 and 2005

By Map Win program, the distributions of eachindicator in the 47 prefectures in 1995, 2000, and 2005
can be shown. For example, each map in Figure 1,
Figure 2 and Figure 3 shows the clear geographicaldifference of age-adjusted death rate for male, percapita income, and rate of certification for LTC
need, respectively. In accordance with the CV, thevariations of death rate of people age

Figure 3

65 years

Distributions of rate of certification for LTC needin Japan in 2000 and 2005.

and per capita income continuously widened amongthe 47 prefectures; in contrast, the variations in age-

Based on the quintile distribution data, each SES

adjusted death rates for females, rate of certification

indicator was divided into five levels from lowest (I)for LTC need, and enrollment rate in higher

to highest (V). The mean and SD of mortality andeducation narrowed from 1995 to 2005 (Table 1).

LTC application rate by different levels of education,
income, and employment in 1995, 2000, and 2005 areshown in Tables 2, 3, and 4, respectively. The mean
and SD of mortality by different LTC applicationlevel is shown in Table 5.
Table 6 shows the mean and rank of eachmortality and LTC indicator of the nine Japaneseregions in 1995, 2000, and 2005. During this period,
variations in mortality indictors in different regions
were identified. A stable, higher age-adjusted death
rate for both males and females was observed in

Figure 1

Distributions of age-adjusted death rate for male

Tohoku, whereas stable, lower age-adjusted deathin Japan in 1995, 2000, and 2005

rates were observed for both males and females inTable 1

Prefectural mortality, SES, and LTC characteristics of Japan― 162 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

Table 2

Descriptive characteristics of mortality and LTC indicators by prefectural education levels

Table 3

Descriptive characteristics of mortality and LTC indicators by prefectural income levels

Table 4

Descriptive characteristics of mortality and LTC indicators by total employment levels

Okinawa and Chubu from 1995 to 2005. The death
Table 5rates of people age

65 years were consistently

Descriptive characteristics of mortality indicators by prefectural LTC application rates― 163 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

high in Shikoku and Chugoku, while they were low

In general, the variations in the 47 prefectures

in Okinawa, Hokkaido, and Kanto from 2000 to 2005.

and 9 regions showed higher SES levels and lower

In Kyushu and Chugoku, the rates of certificationmortality and LTC application rates, like in thefor LTC need remained stably high, while in Kanto,

Kanto and Chubu regions; and lower SES levels

Chubu and Tohoku, the rates remained low fromand higher mortality and LTC application rates,

2000 to 2005.

as in Tohoku. However, other regions showed

The mean and rank of each SES indicator of themiscellaneous patterns for these two parameters; fornine Japanese regions in 1995, 2000, and 2005 areexample, Okinawa had a lower SES level and lower

shown in Table 7. Okinawa, Hokkaido, and Tohoku

mortality and LTC application rates.

had consistently lower higher education enrollment
rates; conversely, Kansai and Chubu had stably

3.2

higher enrollment rates in higher education from

Correlation coefficients between mortality, LTC

1995 to 2005. In Okinawa, Kyushu, and Tohoku, theindicators, and enrollment rates in higher education

per capita income remained consistently low, while

are shown in Table 2. In 1995, education level showed

Kanto, Chubu, and Kansai had higher per capita

no significant relationship with mortality. In contrast,income levels from 1995 to 2005. Hokkaido had thein 2000, education level was statistically significantly

most stable lower total employment rate amongand strongly (r=-0.40) associated with age-adjusted

the nine regions; in contrast, Kanto and Chubu had

death rate for males. In 2005, the enrollment rate inhigher total employment rates during this period.

higher education was also negatively and strongly

Table 6

Correlation analysis

Mortality and LTC indicators of 9 regions in Japan― 164 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

Table 7SES indicators of 9 regions in Japan

associated with age-adjusted death rate for males

indicator and total employment rate are shown in(r=-0.50); moreover, the coefficient in 2005 was larger

Table 4. In 1995, a strong, significant correlation

than it was in 2000. Nevertheless, education level did

between total employment rate and age-adjusted

not show a close or significant correlation with age-death rate for males (r=-0.40) was identified. In 2000,

adjusted death rate in females over time.

total employment rate was found to be strongly andRegarding prefectural income level (Table 3), in

significantly associated with age-adjusted death rate

1995, no close correlations were observed between

for males (r=-0.37) and rate of certification for LTC

per capita income and any mortality indicator. In

need (r=-0.60). In 2005, total employment rate had

2000, per capita income was negatively and strongly

significantly negative and strong associations withassociated with age-adjusted death rate for males

age-adjusted death rate for males (r=-0.35) and rate

(r=-0.39) and rate of certification for LTC needof certification for LTC need (r=-0.69). For women,(r=-0.50). In 2005, per capita income had significant

total employment rate was not associated with age-and strong relationships with age-adjusted death rateadjusted death rate over time.

for males, and the coefficient was larger (r=-0.43)

A stronger positive correlation between age-than it was in 2000. Moreover, significant correlations

adjusted death rate for males and LTC application

also appeared between per capita income and death

rate was identified in 2000 (r=0.33). In 2005, there

rates of individuals age

was a stronger positive correlation between the

65 years (r=-0.35) and rates

of certification for LTC need (r=-0.41). Nevertheless,death rate of people age

no association between per capita income and age-certification for LTC need (r=0.34) (Table 5).

65 years and rate ofadjusted death rate was observed for females during
3.3

this period.
Correlation coefficients between each mortality― 165 ―

Regression analysis

Table 8 shows the final results of the simple

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

regression analysis conducted on the age-adjusted

expected to increase, and 10% of the variance could

death rate for males, including independent

be accounted for by the LTC application rate in 2000.

socioeconomic indicators and LTC, which have shown

Table 9 shows the linear regression model ofstatistically significant associations in the correlation

death rate of people age

analysis. There were significant inverse associations

per capita income increased, the death rate of people

between all the socioeconomic factors (enrollment inage

higher education, higher per capita income, and total

10% of the variance of death rate of individuals

employment rate) and the age-adjusted death rate

65 years could be accounted for by the per capita

for males. As shown in Table 8, the enrollment inincome in 2005. Moreover, as the LTC application

higher education in 2005 was the variable that mostrate increased, the death rate of people age

strongly influenced the geographical distribution ofyears was expected to increase.

65 years in 2005. As the65 years was expected to decrease; moreover,65

age-adjusted death rate for males, which accounted

Table 10 shows the final regression models on

for 24% of the variance in these rates. From 1995 to

the rate of certification for LTC need in 2000 and

2005, the effects of enrollment in higher education

2005, respectively. There were inverse associations

and per capita income on the variations in age-

between two socioeconomic factors (per capita

adjusted death rates for males increased. However,income, and total employment rate) and the rates ofin the same period, the effect of total employment

certification for LTC need. Total employment rate inrate decreased. As the LTC application rate2005 was the most influential factor on geographic

increased, the age-adjusted death rate for males was

distribution, with a R2 of 0.47. Per capita income in

Table 8. Results of univariate linear regression analysis: age-adjusted death rate for males associated with SES and LTC.

Table 9. Results of univariate linear regression analysis: death rate of people

65 years associated with SES and LTC.

Table 10 Results of univariate linear regression analysis: rate of application for LTC need associated with SES.

― 166 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

2000 showed a larger influence on the geographic

for males; in 2000 and 2005, all three SES indicators

distribution of rate of certification for LTC need thanwere found to be inversely associated with age-22adjusted death rate for males. The Pearson’s

it did in 2005 (R of 0.34 vs. R of 0.15).

coefficients showed complicated associations between
4．Discussion and conclusion

SES and mortality over time; for example, theWe applied a time trend analysis between 1995

correlation between education and income and age-and 2005 using annual prefectural data to show

adjusted death rate for males became stronger from

SES, LTC, and mortality variations among the 47

1995 to 2005, while the association between total

prefectures in Japan. The study demonstrated that

employment and age-adjusted death rate for males

prefectural mortality was associated with SES

weakened.

factors such as education, income, employment,
4.2

and LTC application rate, and gender differences

associations

between these associations were also observed

With respect to gender differences in theduring this time period.
4.1

Gender differences in SES-mortality

SES, LTC, and mortality variations in Japancorrelation analysis, three SES indicators significantly

Possible contributors to the improved healthassociated with age-adjusted death rate for malesof the Japanese population have been noted in(income, education, and employment) did not show

previous studies. An egalitarian social system andsignificant differences for females during the period

culture appear to contribute substantially throughfrom 1995 to 2005. Although indicators such as deathcompulsory education, universal health insurance

rate of people

coverage, public health services, income-adjusted

for LTC were not differ by gender, the results

policy, and strong social relationships to further

suggested that prefectural SES influenced healthimprove the health of the population8）, 9）, 10）. The

65 years and rate of applicationlevel more strongly in men than in women. The

egalitarian society, however, may be changing.

finding that male mortality was predicted by SES

The economic recession that followed the collapse

more strongly than female mortality is supported byof the bubble economy in the early 1990s and theprevious studies16）, 17）. There are several plausible

subsequent policies on economics, taxation, andexplanations for this tendency. Firstly, males weresocial security might have contributed to increased

more sensitive to socioeconomic status than females.

socioeconomic inequalities. Following the crumbling

Secondly, it could be related to lifestyle factors suchof the lifetime employment system in Japaneseas smoking, unhealthy diet, and alcohol consumption.

companies, the increase in unstable employment, andLifestyle factors are important pathways throughthe increase in social security costs might have also

which SES adversely influences health 18）, 19）. In

accelerated worries about increasing socioeconomic

Japan, the percentage of individuals with unhealthy11）, 12）, 13）inequalities

lifestyle characteristics, such as smoking and alcohol.

SES has been shown, by cross-sectional,

consumption, is strikingly lower in females than inlongitudinal, and ecological studies, to be a primarymales20）. The relatively healthy lifestyle in womend e t e r m i n a n t o f h e a l t h 14）, 15）. T h e d e g r e e o f

may weak the association between SES and age-socioeconomic inequalities in a society is closely

adjusted death rate for females. Thirdly, complicatedlinked to the health of the population. The results

associations existed between mortality and theobtained by correlation analysis and regression

different SES indicators. Fukuda et al. (2004) reported

analysis in this study revealed the explanatorythat male mortality is more strongly correlatedsocioeconomic factors for mortality and LTC

with income- and education-related indictors than

indicators. In 1995, only the total employment

is female mortality; in contrast, female mortality

displayed correlation with age-adjusted death rate

showed a stronger correlation with living space-― 167 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

related indicators than did male mortality, due to21）

that in Japan, employment and income of older. Lastly,

people are more important predictors for decreasing

combined with some individual-level studies, men

the LTC need compared to education level. Therea longer time spent at home by females

are more likely to die earlier than women are, andare a few plausible explanations for this tendency.

mortality may be strongly influenced by one’s sense

One is the indicator for education that was used—of well-being in men, while it is more likely caused byenrollment rate in higher education—which has a22）,

weak effect on application for LTC services for the. All the above factors may lead to the genderelderly. Another explanation addressed the indirect

actual physical and mental health status in women23）

effect of education on LTC application, because

differences in the direct effect of SES on mortality.

educational attainment as a primary indicator of
4.3

SES effect on mortality and LTC of the

SES would shape the ability to get a good job, earnelderly

more money, and become informed about healthy

The present study revealed that the association

lifestyles. Expected years of life without care needs

65 years andwere calculated and prefectural distributions wereprefectural per capita income became stronger

reported in previous studies 29）, 30）; however, theand significant in 2005. Two other SES indicators,

association between SES and LTC need for theeducation and employment, are associated in all age

Japanese elderly remains unclear and warrants

groups, while the death rate of people age

65 yearsfurther examination. In the LTC field, a consensus

was an indicator for this older population. Thus,

exists that disability among the elderly is thethis gap may explain the insignificant associations.

primary factor driving the demand for LTC services,

Nevertheless, the SES effect on mortality of theand there appears to be a consistent inverse

elderly should be more closely examined, particularly

relationship between SES and disability31）, 32）.

between death rate of people age

in recent years. Previous studies on health inequality
have primarily focused on the relationship between

4.4

SES and premature mortality, and the association

The possible geographic differences among areas

tended to be stronger in the younger population24）.

within a country are of great interest to public health

The weak relationship between SES and mortality inand health policy as they show the potential forthe elderly population could be primarily explained

prevention that still exists. The finding of differencesby selective survivor bias, in which vulnerable

in health within a country, for example, should

people are likely to die before becoming elderly; thus,

suggest consideration of what factors affecting suchelderly people are less vulnerable and represent

variations can be modified33）. The national healthhealthier survivors25）, 26）. However, the relationshipplan in Japan, “Health Japan 21,” accompanied

is not always weaker in the elderly people, andby local actions plans, aims to prolong HALE andinconsistent but substantial evidence exists regarding

eliminate health inequality at both the nationala relationship between SES and mortality or

and local levels through disease prevention and27）, 28）

health promotion. Thus, studying health disparities

morbidity in the elderly.

Study limitations

In this study, rate of application for LTC needby different SES and their trends over time can

was chosen as the indicator of prefectural LTC

play an important part in future health policy.

level and disability status which was found to be

Findings of this study were obtained with reliable

significantly associated with mortality. In addition,mortality, LTC, and SES data at the prefectural

the rate of application for LTC need of older people

level. Nonetheless, the results should be viewed withhad associations with employment and income atcaution.

the prefectural level, but it was not associated with

One limitation is the chosen indicators in thiseducation level in either 2000 or 2005. This suggests

study. Because the selection of both mortality and― 168 ―

社会医学研究．第 31 巻 2 号．Bulletin of Social Medicine, Vol.31（2）2014

SES variables may produce different result patterns,2）Wilkinson RG. Income distribution and life

more specific indicators should be selected for

expectancy. British Medical Journal, 1992；304：

analysis, such as cause-specific death rate, which

165―168.

could help elucidate more detailed information of3）Fukuda Y, Nakamura K & Takano T. Municipal

mortality. Prefectural indicators representing SES

socioeconomic status and mortality in Japan:

have not yet been established in Japan, unlike insex and age differences, and trends in 1973-1998.

some countries where indicators such as deprivation

Social Science and Medicine, 2004；59：2435―

indices have been applied34）. In this study, the2445.

SES in a prefecture consists of various aspects4）F u k u d a Y , N a k a m u r a K & T a k a n o T .

(income, education, and employment) that are

Cause-specific mortality differences acrosscorrelated with each other and influence mortality

socioeconomic position of municipalities in Japan,

and LTC in complicated manners. Thus, univariate

1973―1977 and 1993–1998: increased importance

regression analysis was applied only considering theof injury and suicide in inequality for ages undermulticollineartiy effects. As a result, the development

75. International Journal of Epidemiology, 2005；of prefecture-based socioeconomic indicators is an

34：100―9.

urgent challenge for the study of health inequalities

5）Fukuda Y, Nakamura K & Takano T. Higher

in Japan.

mortality in areas of lower socioeconomic

The second limitation is that the observation

position measured by a single index of

period is too short to conclude that health inequalities

deprivation. Public Health, 2007；121：163―173.

increased since 1995. Health inequalities should be6）Szanton SL, Seplaki CL, Thorpe Jr RJ, Allen JK

continuously monitored.

& Fried LP. Socioeconomic status is associatedThirdly, for natural phenomena, ecologicalwith frailty: the Women’s Health and Aging

studies have methodological limitations, including

Studies. Journal of Epidemiology Community

confounding factors and fallacy

35）

Health, 2010；64：63―7.

. However, theobjective is neither to draw conclusions about the7）Vanden Bosch K, Geerts J & Willemé P. Long-

factors determining the health of people, nor toterm care use and socio-economic status inestablish causal relationships of population health

Belgium: a survival analysis using health carewith its related factors, but rather to identify the

insurance data. Arch Public Health, 2013；71:1.

socioeconomic factors involved in the differences8）Kawachi I & Kennedy BP. The health of thein mortality distribution at the prefectural level,

nations: why inequality is harmful to your health.

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4.5

Conclusions

dimensions of health and equity. In: Evans T,

In conclusion, the factors that explain the mortality,

Whitehead M, Diderichsen F, Bhuiya A & Wirth

LTC, and SES variations in Japan demonstrated that

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lower LTC application rates had lower death rates.

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