International Journal ofEnvironmental Researchand Public Health
ArticleAssociations between Healthcare Resources and
Healthy Life Expectancy: A Descriptive Study acrossSecondary Medical Areas in Japan
Rikuya Hosokawa 1, * , Toshiyuki Ojima 2 , Tomoya Myojin 3 , Jun Aida 4,5 , Katsunori Kondo 6,7,8
and Naoki Kondo 9,10
1
2
3
4
5
6
7
8
9
10

*

Department of Human Health Sciences, Graduate School of Medicine, Kyoto University,
Kyoto 606-8507, Japan
Department of Community Health and Preventive Medicine, Hamamatsu University School of Medicine,
Shizuoka 431-3192, Japan; ojima@hama-med.ac.jp
Department of Public Health, Health Management and Policy, Nara Medical University, Nara 634-8521,
Japan; motoya1014@gmail.com
Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and
Dental University, Tokyo 113-8549, Japan; junaida916@gmail.com
Division for Regional Community Development, Liaison Center for Innovative Dentistry, Graduate Schoolof Dentistry, Tohoku University, Miyagi 980-8575, Japan
Center for Preventive Medical Sciences, Chiba University, Chiba 263-8522, Japan; kkondo@kkondo.net
Center for Well-being and Society, Nihon Fukushi University, Aichi 470-3295, Japan
Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology,
Aichi 474-8511, Japan
Department of Health and Social Behavior, School of Public Health, The University of Tokyo, Tokyo
113-0033, Japan; nkondo@m.u-tokyo.ac.jp
Department of Health Education and Health Sociology, School of Public Health, The University of Tokyo,
Tokyo 113-0033, Japan
Correspondence: hosokawa.rikuya.4r@kyoto-u.ac.jp; Tel.: +81-75-751-4154

Received: 31 July 2020; Accepted: 26 August 2020; Published: 29 August 2020


Abstract: Japan has the highest life expectancy in the world. However, this does not guarantee
an improved quality of life. There is a gap between life expectancy and healthy life expectancy.
This study aimed to reveal the features of healthy life expectancy across all secondary medical areas(n = 344) in Japan and examine the relationship among healthcare resources, life expectancy, andhealthy life expectancy at birth. Data were collected from Japan’s population registry and long-term
insurance records. Differences in healthy life expectancy by gender were calculated using the Sullivan
method. Maps of healthy life expectancy were drawn up. Descriptive statistics and correlation
analysis were used for analysis. The findings revealed significant regional disparities. The numberof doctors and therapists, support clinics for home healthcare facilities and home-visit treatments,
and dentistry expenditure per capita were positively correlated with life expectancy and healthy life
expectancy (correlation coefficients > 0.2). They also revealed gender differences. Despite controllingfor population density, inequalities in healthy life expectancy were observed, highlighting the need topromote social policies to reduce regional disparities. Japanese policymakers should consider optimallevels of health resources to improve life expectancy and healthy life expectancy. The geographicaldistribution of healthcare resources should also be reconstituted.
Keywords: life expectancy; healthy life expectancy; Sullivan method; healthcare resources; secondary
medical area; Japan

Int. J. Environ. Res. Public Health 2020, 17, 6301; doi:10.3390/ijerph17176301

www.mdpi.com/journal/ijerph

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1. Introduction
Although life expectancy (LE) is an indicator of health status, there is a growing interest in thequality of life (QOL) of older adults during their later years. Healthy life expectancy (HLE) is a usefulindicator of a population’s overall health, reflecting length of life as well as QOL [1–3]. HLE refers to
an individual’s length of life lived without limitations in daily activities. In addition, HLE at birth is an
important indicator of a population’s health status and QOL [4]. Moreover, HLE combines data on
both mortality and morbidity [5]. It summarizes mortality and non-fatal outcomes in a single measureof the general population’s health.
The increasing age of the global population warrants greater attention to disorders. Althoughboth LE and HLE have improved, LE with disability has also increased. In an aging society, as greaterage puts increased pressure on social systems, extending HLE and shortening LE with disability
are becoming global priorities [6]. Moreover, LE and HLE differ; longer LE increases the risk fordisability [7,8]. Meanwhile, HLE increases more slowly than LE [9]. Efforts to shorten this gap andencourage healthy aging are needed. Moreover, HLE has been used to compare the health status ofvarious populations and explore health inequalities within a given population. Inequalities in LE andHLE can be observed across multiple regions and countries. However, little is known regarding thedeterminants of inequalities in HLE at the regional level.
According to the World Health Organization (WHO), Japan had the highest average estimated LEat birth in 2016 with 84.2 years [10]. The LE of females in Japan was ranked first in the world with
87.2 years, while the LE of men was ranked sixth with 81.1 years. Similarly, Japan had the highest
average HLE in 2016 with 74.8 years. However, the factors attributed to Japan’s longevity have beena source of debate. It is difficult to identify a specific factor since there are possibilities of interplay
among various factors, such as healthcare system and lifestyle. According to studies, some of thepossible factors include the country’s high living standards, medical advances, and the universal andaccessible healthcare system [11,12].
Japan has achieved satisfactory population health at a reasonably low cost; in fact, the Japanese
have universal health coverage with their National Health Insurance system. Although Japan has thehighest levels of LE and HLE among the members of the Organisation for Economic Co-operation andDevelopment (OECD), its healthcare expenditure as a share of Gross Domestic Product is below that ofmost OECD countries [13]. In addition, regarding lifestyle, the Japanese diet has a relatively lower
calorie and fat intake compared to that of developed economies, such as Europe and North America.
Several studies suggest that adopting a Japanese diet tends to reduce the risk of cardiovascular disease
and metabolic syndrome [14,15].
However, on average, the Japanese spend the last 9.4 years (11.2% of LE) of their lives with poor
health, mobility impairments, or in a bedridden state. The duration of the difference between LE andHLE represents the average number of years with poor health (i.e., unhealthy life expectancy) [16].
In Japan, there is still a gap between LE and HLE [17]. Although the Japanese have the highest LE and
HLE in the world, unhealthy life expectancy is not necessarily at the highest levels (e.g., Singapore:
6.8 years; 8.2% of LE, Spain: 9.3 years; 11.2% of LE, Switzerland: 9.8 years; 11.8% of LE) [10]. In addition,
having high LE does not necessarily mean high QOL. Thus, in recent years, the Japanese government
has focused on extending HLE.
Extension of HLE was one of the main goals presented in “The Second Term of the National
Health Promotion Movement in the Twenty First Century” (Health Japan 21, the Second Term) [18,19].
This 10-year nationwide health promotion project from 2013 to fiscal year 2022 was developed by
the Ministry of Health, Labour, and Welfare with two main goals: extending HLE and reducing
health inequality. Health inequality refers to the gap in HLE among prefectures (Japan is divided
into 47 administrative districts known as prefectures). A 2016 study calculated the HLE for eachprefecture using data from the Comprehensive Survey of Living Conditions and reported a differenceof 2.70 years for females and 2.00 years for males [20]. Although Japan has been successful in theirhealth outcome statuses (e.g., reducing mortality and disability), variations in health outcome statuses

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between prefectures are increasing [21]. In fact, the government is considering setting a target healthexpenditure level for each prefecture to address the issue of rapid increase and regional variations inhealth expenditure. Although small municipal area analyses were conducted, the standard errors were
too large. Furthermore, the population of several municipal areas is too small to provide medical care.
Thus, to implement effective health policies, further descriptive analyses in smaller areas are required.
It is important to clarify the difference in HLE in each healthcare accessible area and implement
effective policies.
In Japan, healthcare service areas have been established to provide efficient medical services.
Under the Medical Care Law, these areas are required to provide general healthcare supplies throughtheir prefectural governments [22,23]. Primary medical service areas consist of approximately 1700
districts; secondary medical areas consist of 344 jurisdictions; and tertiary medical service areas consistof 52 areas. The secondary medical area is a region where general inpatient medical care can beprovided. In addition, the medical provision system is planned based on area unit to maintain medical
resources (e.g., number of beds and number of clinic facilities). The characteristics of each region withrespect to HLE by prefecture levels have been reported in multiple studies. However, these studies
have focused on health promotion and care prevention measures. There is a lack of assessment in unitsof administrative districts that are directly linked to administrative activities.
The Japanese population has been rapidly aging because of decreasing birth rates and increasing
LE. In fact, Japan has the highest proportion of older adults in the world [24]. In 1999, the populationof those aged 65 years and older was 16.7% [25]. In 2019, older adults comprised 28.4% of the total
population [26]. This figure is expected to increase to 35.3% by 2040, 37.7% by 2050, and 38.1% by
2060 [27]. Rapid aging has a substantial effect on disease structure, which is leading Japan to an advanced
stage of epidemiological transitions [12,28]. Older adults experience a higher number of chronic
diseases and multiple morbidities that require long-term care and increase healthcare expenditure.
However, the characteristics of population aging and epidemiological transition differ by region,
which can have a significant impact on healthcare system performance [29]. These factors influence thesocial determinants of health and may increase imbalances within the healthcare system [30]. Thus, it isimportant to develop a healthcare system that matches the actual conditions of each region. However,
the actual distribution of the difference in HLE among secondary medical areas has yet to be clarified.
Therefore, understanding the regional characteristics of the medical service system related to HLE insecondary medical areas is important to have a more effective approach in extending HLE and healthy
aging policies.
In Japan, one of the most important policy challenges is the creation of an economically active
aging society and a strong healthcare system to sustain it. Although the medical provision system insecondary medical areas is designed to be modified as necessary, it cannot always address regional
disparities in healthcare resource distribution [31]. Japan is ranked 18th among OECD member states for
low-cost medical care, and ranked first for its high level of health attainment and HLE [32,33]. Japan has
one of the best healthcare systems in the world in terms of availability and cost. Many researchers
attribute the success of its healthcare system to the dexterous balance of supply and demand andcontrol over medical costs through the universal health insurance system [12,13]. However, Japan’s
super-aging population is putting more pressure on its health system’s sustainability. Although
the characteristics of the Japanese healthcare system have the advantages of healthcare availability,
such healthcare systems cannot meet the needs of a super-aging society. Prior studies suggest that LE is
influenced by healthcare resources, such as hospital bed capacity, healthcare workforce, and healthcare
expenditure [34,35]. Healthcare resources have short- and long-term effects on health, which have beenstudied in terms of health output (e.g., number of medical doctors) or health outcomes (e.g., LE andmortality rate). However, the effects of healthcare resources on the average LE and HLE have not been
fully elucidated.
Ultimately, good overall population health is key to a productive and developed society. Healthcare
resources, including number of beds and workforce size, is one of the most important factors that

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influence health status [36]. Although Japan’s healthcare system is the most organized in the world,
there are functional differences between regions that need to be addressed [37]. There is a gap interms of equal distribution of beds, staff, and doctors among all prefectures. Thus, it is necessary
to clarify the relationship between medical care resources (i.e., hospital beds, healthcare workforce,home healthcare service, and healthcare expenditure) and HLE to contribute to developing effective
policies to extend HLE.
Current Study
This study aimed to identify the descriptive features of the distribution of HLE across secondary
medical areas in Japan. In addition, it sought to clarify the relationship between healthcare resources,
LE, and HLE through a geographical study of all secondary medical areas in Japan. The findings may
have many implications for other countries with super-aging societies. However, research into thedescriptive features of HLE and the associations between LE, HLE, and healthcare resources in thecontext of Japan is limited. This study comes at a time when health policymakers are reviewing andassessing priorities for action in Japan. It supports mapping of HLE in Japan, and the findings may
contribute to the development of national and region-specific health policies for healthcare resources.
2. Materials and Methods
2.1. Measures
2.1.1. Outcome Variables: Life Expectancy and Healthy Life Expectancy
We estimated HLE across all 344 secondary medical areas in Japan using the Sullivan method,which is based on age-specific death rates and years lived with disability [38]. In this method,
by applying age-specific prevalence rates of a particular unhealthy state to a life table function (i.e.,
number of person-years lived in each age interval), the total LE is divided into person-years lived withgood health (i.e., HLE). HLE is a measure of population health that estimates the expected number of“healthy years” (i.e., years spent in good health) of individuals at a given age. In addition, LE at birth
was calculated using the population age standard.
To calculate LE and HLE, population data as of 2017 were obtained from the resident registry ofJapan [39]. Mortality data were obtained from the Vital Statistics of Total Deaths in 2016–2018 [40].
Data on care needs were obtained from the Report on Long-Term Care Insurance Services. In thisstudy, “healthy” was defined as the period of time spent without limitation in daily activities, while“unhealthy” was the period of time spent with limitations in daily activities. The Japanese care system
is divided into care levels from 1 through 5, based on individuals’ care needs as certified by Japan’slong-term care insurance system (see Table S1) [41,42]. Data on unhealthy people, which included thoseat care level 2 (almost bedridden) and higher, were obtained from the 2017 long-term care insurance
data [43]. The present study classified those at level 2 or greater as “having care needs” (i.e., unhealthy);
all other levels were classified as “almost no care needs” (i.e., healthy).
2.1.2. Explanatory Variable: Healthcare Resources
In this study, we used healthcare resources (i.e., hospital beds, healthcare workforce, homehealthcare service, and healthcare expenditure) as the explanatory variable. The population ratio of
healthcare resources across all 344 secondary medical areas was calculated [44–46]. We used datafrom the Survey of Medical Institutions, Annual Report on Health, Labour and Welfare, and National
Database of Health Insurance Claims and Specific Health Checkups of Japan [44–46]. Hospital beds
(i.e., number of beds) included curative (acute), rehabilitative, and long-term care beds per 1000
residents. The healthcare workforce (i.e., number of health professionals) consisted of doctors, nurses,
and therapists per 1000 residents. Therapists included physical, occupational, and speech-hearing
therapists. Home healthcare services (i.e., number of facilities) included support hospitals and clinics

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for home healthcare per 1000 residents, and home-visit care facilities per 1000 residents. The standard
for healthcare facilities established by the Ministry of Health, Labour and Welfare requires hospitals andclinics to supplement home healthcare; medical facilities support patients living at home by providing
medical care 24 hours a day [47]. A facility with at least 20 beds is considered a “support hospital ofhome health care”, while a facility with fewer than 20 beds is a “support clinic of home health care”.
2.1.3. Confounding Variable: Population Density
Studies show that population density is significantly related to healthcare resources [37,48,49].
Considering that the location of healthcare resources is largely determined by the population of the area,
we investigated the associations between healthcare resources, LE, and HLE at birth by performing
partial correlation analyses after controlling for population density using data obtained from the
Population Census of Japan [50]. Since the population density has a logarithmic normal distribution,
the logarithm of the population density was calculated.
2.2. Statistical Analyses
IBM SPSS Statistics Version 26 (IBM Corp, Armonk, NY, USA) was used for data analyses.
To examine the descriptive features of the distribution of HLE across secondary medical areas in Japan,
it was divided into five categories by 20th percentiles and gender. Moreover, maps of HLE levels acrossJapan were drawn [51]. A chi-squared test was performed to assess the differences in HLE among
eight regions (i.e., Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku, Shikoku, and Kyushu-Okinawa
regions) by gender.
Associations between healthcare resources, LE, and HLE at birth were assessed using partial
correlation analyses controlling for population density. In the analysis, correlations were significant at
the p < 0.001 level (two-tailed), and correlation coefficients greater than 0.2 were considered to indicate
a positive correlation.
3. Results
3.1. Descriptive Statistics for Life Expectancy and Healthy Life Expectancy at Birth
Table 1 shows the descriptive statistics for LE and HLE at birth in years. The mean HLE atbirth was 79.21 years (standard deviations 0.85) for males and 83.75 years (standard deviations 0.62)
for females. Furthermore, a comparison of HLE in secondary medical areas revealed differences of
4.46 years (minimum 76.90, maximum 81.36) for males and 3.46 years (minimum 81.99, maximum
85.45) for females.
The nationwide distributions of HLE are presented in Figure 1 for males and Figure 2 for females.
In the current study, chi-squared tests were performed to analyze the differences in HLE among eightregions (i.e., Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku, Shikoku, and Kyushu-Okinawa
regions) by gender. For males, there were significant differences in HLE among the regions (p < 0.001).
The proportion of secondary medical areas with short HLE tended to be higher in the northern part ofJapan (Hokkaido and Tohoku regions), while the proportion of areas with high HLE was higher inthe central (Chubu region) and west-central (Kinki regions) parts. Similarly, for females, there weresignificant differences in HLE among the regions (p < 0.001). The proportion of secondary medical
areas with short HLE was higher in the northern (Tohoku region) and northern-central (Kanto region)
parts of Japan, while the proportion of areas with high HLE was higher in the central (Chubu region)and southern (Kyushu-Okinawa regions) parts. These results indicated significant regional disparities;
the common point is that for both males and females, the proportion of areas with short HLE was highin the northern part of Japan (Tohoku region), while the proportion of areas with high HLE was highin the central part (Chubu region).

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Chi-square test: p < 0.001≤79.46 (≤60th percentile）
Chi-square test: p < 0.001≤79.46 (≤60th percentile）

Figure 1. Distribution of healthy life expectancy for males in each secondary medical area. Note:
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Chi-square test: p < 0.001≤83.90 (≤60th percentile)
Chi-square test: p < 0.001≤83.90 (≤60th percentile)

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Table 1. Descriptive statistics for life expectancy and healthy life expectancy (years) at birth.
Males
Variables
Life expectancy
Healthy life expectancy

Females

M

SD

Min

Max

M

SD

Min

Max

80.62
79.21

0.87
0.85

78.30
76.90

82.79
81.36

86.87
83.75

0.62
0.62

85.05
81.99

88.36
85.45

Note: Life expectancy and healthy life expectancy were calculated by the Sullivan method; M: Mean; SD: Standard
deviation; Min: Minimum value; Max: Maximum value.

3.2. Associations Between Healthcare Resources, Life Expectancy, and Healthy Life Expectancy at Birth
Data for healthcare resource variables (i.e., hospital beds, healthcare workforce, community
healthcare service, and healthcare expenditure) are shown in Table 2. Associations between healthcare
resources, LE, and HLE at birth are shown in Tables 3 and 4. For both males and females, the numbersof hospital beds were not correlated with LE and HLE, and no significant correlation was observed
according to the criteria of this study (i.e., correlations were significant at the p < 0.001 level,
and correlation coefficients greater than 0.2 were considered to indicate a positive correlation).
However, for males, the number of curative (acute) care beds per 1000 residents tended to correlate
negatively with LE (r = −0.122, p = 0.023) and HLE (r = −0.129, p = 0.017). In contrast, for females,
the number of rehabilitative care beds per 1000 residents tended to correlate positively with LE(r = 0.126, p = 0.019) and HLE (r = 0.157, p = 0.003), and long-term care beds per 1000 residents tended
to correlate positively with HLE (r = 0.117, p = 0.030).
Table 2. Descriptive statistics of healthcare resource variables.
Variables

M

SD

Min

Max

6.06
1.41
3.45

1.77
0.83
2.31

1.57
0.00
0.00

14.19
5.05
17.33

0.87
2.87
0.57

1.12
3.95
0.08

11.89
19.61
4.30

0.01
0.10
0.20

0.01
0.06
0.08

0.00
0.00
0.06

0.10
0.35
0.47

153,129.53
195,347.25
23,966.78

31,146.30
17,342.34
2887.87

91,904.53
135,962.50
14,294.20

253,059.03
276,722.07
32,158.29

Hospital beds (number of beds per 1000 residents)
Curative (acute) care
Rehabilitative careLong-term care

Healthcare workforce (number of healthcare providers per 1000 residents)
Doctors
Nurses
Therapists

2.45
9.55
1.16

Home healthcare service (number of facilities per 1000 residents)
Support hospital of home healthcare
Support clinic of home healthcare
Home-visit care facility
Healthcare expenditure (JPY per capita)
Hospitalization
Outpatient
Dentistry

Note: The facilities with at least 20 beds are “support hospital of home health care”, while the facilities with fewer
than 20 beds are “support clinic of home health care”. Therapists include physical therapists, occupational therapists,and speech-hearing therapists. JPY: Japanese yen, M: Mean, SD: Standard deviation; Min: Minimum value; Max:
Maximum value.

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Table 3. Associations between healthcare resources, life expectancy, and healthy life expectancy at birthfor males.
Life Expectancy

Variablesr

p Value

Healthy Life Expectancy
dfr

p Value

df

341
341
341

−0.129
0.034
0.014

0.017
0.533
0.801

341
341
341

0.046
−0.044
0.115

0.395
0.421
0.033

341
341
341

Hospital beds (number of beds per 1000 residents)
Curative (acute) care
Rehabilitative careLong-term care

−0.1220.022−0.010

0.023
0.686
0.860

Healthcare workforce (number of healthcare providers per 1000 residents)
Doctors
Nurses
Therapists

0.076−0.046
0.099

0.163
0.396
0.067

341
341
341

Home healthcare service (number of facilities per 1000 residents)
Support hospital of homehealthcare
Support clinic of homehealthcare
Home-visit care facility

0.106

0.051

341

0.106

0.049

341

0.155

0.004

341

0.154

0.004

341

0.145

0.007

341

0.134

0.013

341

0.964
<0.001<0.001

341
341
341

0.004
0.176
0.237 *

0.940
0.001
<0.001

341
341
341

Healthcare expenditure (JPY per capita)
Hospitalization
Outpatient
Dentistry

0.002
0.180
0.228 *

Note: Partial correlation analyses were performed by controlling for population density; * Correlations weresignificant at the p < 0.001 level (two-tailed), and correlation coefficients greater than 0.2 were considered to indicate
a positive correlation.

Table 4. Associations between healthcare resources, life expectancy, and healthy life expectancy at birthfor females.
Life Expectancy

Variablesr

Healthy Life Expectancy

p Valuedfr

p Value

df

0.460
0.019
0.208

341
341
341

0.018
0.157
0.117

0.746
0.003
0.030

341
341
341

341
341
341

0.154
0.134
0.242 *

0.004
0.013
<0.001

341
341
341

Hospital beds (number of beds per 1000 residents)
Curative (acute) care
Rehabilitative careLong-term care

0.040
0.126
0.068

Healthcare workforce (number of healthcare providers per 1000 residents)
Doctors
Nurses
Therapists

0.220 *
0.119
0.198

<0.001
0.027<0.001

Home healthcare service (number of facilities per 1000 residents)
Support hospital of home healthcare
Support clinic of home healthcare
Home-visit care facility

0.182
0.226 *
0.268 *

<0.001<0.001<0.001

341
341
341

0.190
0.222 *
0.231 *

<0.001<0.001<0.001

341
341
341

0.192
0.173
0.132

<0.001
0.001
0.015

341
341
341

0.180
0.141
0.131

<0.001
0.009
0.015

341
341
341

Healthcare expenditure (JPY per capita)
Hospitalization
Outpatient
Dentistry

Note: Partial correlation analyses were performed by controlling for population density; * Correlations weresignificant at the p < 0.001 level (two-tailed), and correlation coefficients greater than 0.2 were considered to indicate
a positive correlation.

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Regarding the healthcare workforce, there was no significant positive correlation with LE and HLE
for males; however, therapists per 1000 residents tended to be positively correlated with HLE (r = 0.115,
p = 0.033). For females, doctors per 1000 residents were significantly positively correlated with LE(r = 0.220, p < 0.001), and therapists per 1000 residents were significantly positively correlated with
HLE (r = 0.242, p < 0.001). Meanwhile, doctors per 1000 residents tended to correlate positively with
HLE (r = 0.154, p = 0.004), nurses per 1000 residents tended to correlate positively with LE (r = 0.119,
p = 0.027) and HLE (r = 0.134, p = 0.013), and therapists per 1000 residents tended to correlate positively
with LE (r = 0.198, p < 0.001).
In terms of home healthcare service, there were no significant correlations with LE and HLE
for males. However, support hospitals for home healthcare per 1000 residents tended to correlate
positively with HLE (r = 0.106, p = 0.049). Moreover, support clinics for home healthcare and home-visit
care facilities per 1000 residents tended to correlate positively with LE (r = 0.155, p = 0.004 and r = 0.145,
p = 0.007, respectively) and HLE (r = 0.154, p = 0.004 and r = 0.134, p = 0.013, respectively). For females,
support clinics for home healthcare and home-visit care facilities per 1000 residents were significantly
positively correlated with both LE (r = 0.226, p < 0.001 and r = 0.268, p < 0.001, respectively) and HLE(r = 0.222, p < 0.001 and r = 0.231, p < 0.001, respectively). Support hospitals for home healthcare per1000 residents tended to correlate positively with LE (r = 0.182, p < 0.001) and HLE (r = 0.190, p < 0.001).
For healthcare expenditure, for males, dentistry expenditure per capita was significantly positively
correlated with both LE (r = 0.228, p < 0.001) and HLE (r = 0.237, p < 0.001), while outpatient expenditure
per capita tended to correlate positively with LE (r = 0.180, p < 0.001) and HLE (r = 0.176, p = 0.001).
For females, healthcare expenditure did not have a significant correlation with LE and HLE. However,hospitalization, outpatient, and dentistry expenditure per capita tended to correlate positively with LE(r = 0.192, p < 0.001, r = 0.173, p = 0.001, and r = 0.132, p = 0.015, respectively) and HLE (r = 0.180,
p < 0.001, r = 0.141, p = 0.009, and r = 0.131, p = 0.015, respectively).
4. Discussion
This study aimed to reveal the descriptive features of the distribution of HLE across secondary
medical areas in Japan. In addition, the relationship among healthcare resources, LE, and HLE wasexamined through a geographical study of all secondary medical areas in Japan. The findings revealed
significant regional disparities regarding HLE despite controlling for population density. This indicatesthe need for action and execution of programs aimed at reducing regional disparities. For males,
the proportion of secondary medical areas with short HLE was higher in the northern part of Japan(Hokkaido and Tohoku regions), while areas with higher rates of high HLE were in the central (Chubu
region) and west-central (Kinki regions) parts. For females, the proportion of secondary medical
areas with short HLE was higher in the northern (Tohoku region) and northern-central (Kanto region)
parts of Japan, while areas with higher rates of high HLE were in the central (Chubu region) andsouthern (Kyushu-Okinawa regions) parts. The above results indicate that for both males and females,
the proportion of areas with short HLE was higher in the northern part of Japan (Tohoku region), andthe areas with higher rates of high HLE were in the central part (Chubu region). Thus, this suggeststhat there are inequalities in HLE across secondary medical areas in Japan.
Previous research suggests that healthcare resources, including the number of beds, can affecthealth. However, in this study, hospital beds were not significantly correlated with LE and HLE for
either males or females at birth. This lack of correlation might be explained by the fact that hospitalbeds are well-deployed in Japan [52,53]. Although there was no significant association between thenumber of beds and LE and HLE, a negative trend for acute care beds and health outcomes wasobserved; that is, for males, curative (acute) care beds per 1000 residents were negatively correlatedwith LE and HLE. This finding is consistent with previous studies that found that providing an
excessive number of beds was negatively associated with health outcomes [34]. Overall, the level ofhealth resources, including human and physical resources, is positively correlated with better healthoutcomes (i.e., decreased death rates and prolonged life). Ultimately, policymakers should consider

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the optimal levels of health resources in terms of number of physicians, nurses, and beds to achieve
better health outcomes. In addition, they should consider the potential negative effects of oversupply
on population health.
Regarding the healthcare workforce, for females, doctors per 1000 residents were significantly
positively correlated with LE and tended to correlate positively with HLE; conversely, therapists per1000 residents were significantly positively correlated with HLE and tended to correlate positively
with LE. For males, therapists per 1000 residents tended to correlate positively with HLE. Prior studies
suggested that healthcare resources have short- and long-term effects on health. In addition, multiple
studies revealed that countries with higher levels of human health resources (e.g., number of physicians)
typically have better overall population health (e.g., LE and mortality rate) [54]. Studies on international
differences on the impact of the number of doctors on health reported that doctors per capita were an
important determinant of mortality [55–57]. Mortality rate has a negative correlation with the numberof doctors, which seems reasonable since a sufficient number of doctors who can diagnose diseases
and provide appropriate care services can mitigate certain causes of death. Therefore, the number ofdoctors may be related to LE and HLE.
Furthermore, in this study, the number of therapists was also significantly associated with HLE;
Rehabilitative health services are expected to extend HLE, since rehabilitation is aimed at optimizing
individual functions related to health status [58]. Rehabilitation practitioners include physical therapists,
occupational therapists, and speech-hearing therapists. As aging of the population has a serious
impact on social systems, it is important to address people’s functioning, including restricted functions
that affect their daily activities. Considering the effect of healthcare worker availability on health,the need to improve cross-region heterogeneity is significant. Thus, the healthcare workforce can be afacilitating factor of LE and HLE. However, the link between rehabilitation of human resources andhealth outcomes has not been fully validated and further evaluations are required in the future.
Home health resources were related to LE and HLE. For females, support clinics for homehealthcare and home-visit care facilities per 1000 residents had significant positive correlations withboth LE and HLE. On the other hand, for males, support clinics for home healthcare and home-visit
care facilities per 1000 residents tended to be positively correlated with LE and HLE. Visiting medicalcare can be improved by increasing the efficiency and collaboration between care service dealers,clinics, and hospitals. Improving home-based medical care and nursing, which include end-of-life carethat is in keeping with the individual’s values, can enhance both LE and HLE. Studies have notedthat access to a primary care service has a strong and significant influence on longer LE and lower
mortality rates [59,60]. Primary care typically involves a wide range of contents, including prevention
and treatment. Prior studies at the regional level demonstrated that higher availability of primarycare services was associated with fewer incidences of disabilities and mortality [59,60]. Although
the Japanese healthcare system is open and flexible regarding healthcare provision and access [11],it cannot be said that home-based medical care is adequate, as has been noted in this study. Moreover,ranges of distribution in home healthcare service among secondary medical areas were observed inthis study (see Table 2). Home healthcare services can be a facilitating factor of LE and HLE. However,while the effect of hospital healthcare resources on health outcomes has been reported, the link between
home healthcare services and health outcomes has not been fully clarified, thus warranting further
investigation in the future.
Regarding healthcare expenditure, for males, dentistry expenditure per capita was significantly
positively correlated with both LE and HLE; for females, dentistry expenditure per capita tended
to correlate positively with both LE and HLE. Dental expenditure seems to be associated with thelevel of access to oral care. To prevent loss of teeth, it is important to maintain oral health [61,62].
Although the basic step to maintaining oral health is oral self-care, making an accurate self-assessmentof oral health status is difficult. Thus, it is important for older adults to visit dental health experts forregular prevention and treatment. Oral health is one of the key markers of overall health, well-being,and QOL [63]. In addition, oral diseases are risk factors for various diseases [64–66]. For instance,

Int. J. Environ. Res. Public Health 2020, 17, 6301

11 of 16oral diseases are related to physical illnesses, such as infections and cardiovascular diseases. In addition,
missing teeth is a risk factor for decline of cognitive competence [67]. Oral diseases are chronic andcumulative in nature and aggravate over time [68]. In addition, dental care costs may be affected bysocioeconomic factors; socioeconomic differences are associated with LE and healthy and disease-free
LE [69]. These findings concur with the present findings, which highlights the importance of providing
appropriate prevention and treatment services to maintain dental health and functioning in later life.
In this study, regarding the association between healthcare resources and health outcomes, males
had lower or non-significant correlation values compared to females. Lower point estimates among
males compared to females can be explained by the fact that the difference between LE and HLE (i.e.,
unhealthy life expectancy) is higher among females than among males; in this study, the differences were1.41 years and 3.12 years for males and females, respectively. In addition, similar to Japan, unhealthy
life expectancy tends to be higher in females than in males in other countries [10]. In general, males andfemales differ in terms of various health outcomes, including mortality and morbidity [70,71]. In thepresent study, a shorter LE and HLE among males compared to females, as well as other limitations,
may have affected the relationship between LE, HLE, and healthcare resources. Factors related tolifestyle that affect morbidity and mortality, including health-related behavior, might explain the gender
gap in health expectancy [72–78]. However, the predictors of gender differences in environmental
factors and HLE have not been fully clarified. In order to continue developing effective approaches,
it is necessary to identify the factors affecting gender differences.
In this study, the distribution of HLE conducted at the secondary care area level is consistent witha previous study conducted at the prefecture level in Japan [21]. The study conducted at the prefecturelevel suggested that behavioral risk factors made a greater contribution to both disability-adjusted
life years and mortality, and that the most important behavioral risk factors for health outcomes
were unhealthy diet and smoking. Substantial opportunities for a healthier population exist via
modifiable risk factor approaches. However, since it is difficult to identify a specific factor that affects
HLE, there are possibilities of interplay among various factors, including healthcare system andlifestyle [14,15]. In this study, there were no strong associations between healthcare resources and HLE;
however, factors other than healthcare resources could have a possible impact on HLE. People living
in regions with higher HLE might have better lifestyles, including more efficient prevention andtreatment of diseases. Thus, to continue developing effective approaches, it is necessary to identify thefactors from multifaceted viewpoints. It is important to consider not only the optimal levels of healthresources, but also lifestyle factors, in order to improve HLE.
Limitations
This study had several limitations. First, there is a possible bias of geographical fallacy, as thisstudy model was a geographical study. Second, although we analyzed the relationship between
medical resources, LE, and HLE, adjusting for population density alone, other factors were not adjusted;
thus, it is difficult to determine a specific factor. Various other confounding factors may need to becontrolled for in future studies. Third, although the findings of this study concur with some of those ofprevious studies conducted in other countries, it should be noted that this study was conducted onlyin Japan. As all data were collected in Japan, it is difficult to generalize the results to other countries.
5. Conclusions
In the present study, inequalities in HLE were found despite controlling for population density.
This indicates the need to promote social policies designed to reduce regional disparities. As describedabove, some factors related to healthcare resources were significantly related to LE and HLE; thenumbers of doctors and therapists, support clinics for home healthcare facilities and home-visit
treatments, and dentistry expenditure per capita were positively correlated with LE and HLE. Most of
our findings were consistent with previous studies conducted in other countries that have different
levels of healthcare systems; multiple studies have shown that the most important factor that contributes

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to a population’s health status is healthcare resources, which include the number of beds and healthcare
professionals. On the other hand, this study may indicate an important factor in countries withhigh-quality healthcare systems, since there is limited research on the relationship between healthcare
resources, such as healthcare workforce, home healthcare facilities, and home-visit treatments, and HLE.
Ultimately, policymakers should consider the optimal levels of healthcare resources in order to improve
LE and HLE. Our results suggest the necessity to consider the geographical redistribution of healthcare
resources. In addition, further studies are needed to ensure a more effective and balanced geographicaldistribution of healthcare systems in Japan.
Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/17/6301/s1,
Table S1: Types of long-term care (Japanese care system).
Author Contributions: Conceptualization, T.O. and N.K.; Methodology, R.H. and T.O.; Software, R.H. and T.O.;
Validation, R.H. and T.O.; Formal analysis, R.H. and T.O.; Investigation, R.H. and T.O.; Resources, R.H. and T.O.;
Data Curation, R.H. and T.O.; Writing—original draft preparation, R.H.; Writing—review and editing, T.O., T.M.,
J.A., K.K., N.K.; Visualization, R.H. and T.O.; Supervision, T.O.; Project Administration, T.O. and N.K.; Funding
Acquisition, N.K. All authors have read and agreed to the published version of the manuscript.
Funding: This work was funded by Health Labour Sciences Research Grant (grant number 19FA1012) and Health
Labour Sciences Research Grant (grant number 19FA2001).
Acknowledgments: The authors gratefully acknowledge all persons who contributed to this study.
Conflicts of Interest: The authors declare no conflict of interest.

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