International Journal ofEnvironmental Researchand Public Health
ArticleWhat Types of Greenspaces Are Associated with
Depression in Urban and Rural Older Adults?
A Multilevel Cross-Sectional Study from JAGES
Miho Nishigaki 1, * , Masamichi Hanazato 2 , Chie Koga 2
1
2
3

*

and Katsunori Kondo 2,3

Graduate School of Medical and Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku,
Chiba-shi, Chiba 260-8672, Japan
Center for Preventive Medical Sciences, Chiba University, 1-33 Yayoi-cyo, Inage-ku, Chiba-shi,
Chiba 263-8522, Japan; hanazato@chiba-u.jp (M.H.); chiekoga@chiba-u.jp (C.K.); kkondo@kkondo.net (K.K.)
Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology,
7-430 Morikoka-cho, Obu-shi, Aichi 474-8511, Japan
Correspondence: miho-nishigaki@chiba-u.jp; Tel.: +81-43-290-3877

Received: 6 November 2020; Accepted: 9 December 2020; Published: 11 December 2020


Abstract: Depression in older adults is a public health challenge. We aimed to clarify the relationshipbetween depression in older adults and three types of neighborhood greenspaces: trees, grasslands,
and fields. We utilized data from the Japan Gerontological Evaluation Study (JAGES) performed in2016. Multilevel logistic regression analysis was used for non-stratified and stratified analyses forthe urban–rural regions. The target population comprised 126,878 older adults (age ≥ 65 years)
who responded to the depression questions and were living in 881 neighborhoods in Japan.
Depression was diagnosed based on a Geriatric Depression Scale score ≥5, and 20.4% of the study
population had depression. In the pre-stratification analysis, areas with more greenspaces revealed
lower odds of depression (odds ratio (OR) 0.95, 95% confidence interval (CI) 0.85–0.95). In urban
areas, more trees correlated with lower odds of depression (OR 0.94, 95% CI 0.89–1.00). In rural areas,
moderate amounts of grassland were associated with lower odds of depression compared to areas
with fewer grasslands (OR 0.91, 95% CI 0.83–1.00). We found that urban areas with higher tree density
and rural areas with moderate amounts of grassland were associated with lower odds of depression.
Keywords: older adults; depression; mental health; greenspace; trees; grasslands; fields; built
environment; urban; rural

1. Introduction
Depression is considered one of the causes of deterioration in physical and cognitive functions andpremature death. In older adults, it is an important public health challenge [1]. Among all age groups,
the rate of depression is highest in older adults [1]. Reports indicate that mental health improvements
are related to the number of greenspaces in an area [1–7]. For example, sufficient greenspaces in aregion can increase physical activity, social contact, and biodiversity, reduce stress, improve exposure tobetter air quality, and mitigate the heat island effect, which are related to better mental health status [2].
Several studies have focused on the association between greenspace and health; however,research on the types of greenspaces and their associations with older adults’ depression is limited.
A study assessing the difference between trees and grasslands in urban areas found that a large
number of trees increased the subjective health of adults, while the same was not observed for thegrasslands [8]. Having trees in urban areas was also associated with more walking by adults [9].
Moreover, there was a reduction in the rate of depression among older adults living in nursing homes
situated in neighborhoods that had more trees [10]. In a longitudinal analysis, a large number ofInt. J. Environ. Res. Public Health 2020, 17, 9276; doi:10.3390/ijerph17249276

www.mdpi.com/journal/ijerph

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grasslands in rural areas was associated with good mental health in older adults [11]. These findings
indicate that the impact of greenspace on depression among older adults may vary depending on thetype of greenspace and urban and rural characteristics.
Many of these previous studies assessed the relationship between greenspace and mental healthin urban populations only [4,6,7]. Regional differences, such as urban and rural areas, may havedifferent effects on the relationship between the type of greenspace and the health of older adults [11].
For example, considering exposure to trees, for those in urban areas, street and park trees are easy
to access. However, trees in rural areas are often located in places that are difficult to access, like theforests, which makes exposure to trees difficult. Unlike urban areas, grasslands and fields occupy
large areas in rural regions. Thus, greenspace factors related to depression in older adults may differ
between urban and rural regions.
Urban and rural perspectives play an important role in social gerontology [12,13]. Therefore, it isnecessary to study the relationship between the local environment and the health of older adults inboth urban and rural regions. A study on urban and rural differences in China found that older adults
in rural areas had higher levels of depression than those in urban areas [14]. According to a study
comparing urban and rural Japan, older adults in rural areas display lower social participation andhigher dementia risk than those in urban areas [15,16]. Moreover, depression is one of the factors ofdementia in older adults [1]. In this regard, it is necessary to clarify the association between urban
and rural regions and their respective greenspaces with depression in older adults. To the best of ourknowledge, no studies have assessed the association between the types of urban and rural greenspaces
and depression in older adults who do not require long-term care or assistance.
Therefore, the first objective of this study was to clarify the relationship between depression
and the prevalent types of greenspaces (trees, grasslands, and fields) in older adults in Japan. Next,
we clarified whether urban and rural differences in residential areas influenced the relationship between
depression and the type of greenspace.
2. Materials and Methods
2.1. Selection and Description
2.1.1. Description of the Setting
We used cross-sectional data from the Japan Gerontological Evaluation Study (JAGES) 2016 [17,18].
This is a survey of independent men and women over the age of 65 years, not in long-term care.
The survey areas comprised 39 municipalities in 18 prefectures out of 47 prefectures in Japan.
(Figure 1). The municipalities include urban/suburban and rural communities from the north(Hokkaido; the northernmost area) and south (Kyushu; the south area) in Japan. The survey was
conducted between October 2016 and January 2017, and questionnaires were mailed to the potential
participants. The survey participants were extracted on a municipal basis, and the sampling frame
was based on the information of the long-term care insurance certification data for the older adults
aged 65 years and above, excluding those requiring long-term care, depending on the size and budgetof the municipality. Questionnaires were distributed to all participants when the number was lessthan 5000 and to those selected by random sampling when the number was 5000 or more. Of the279,661 questionnaires distributed, 196,091 (effective collection rate, 70.1%) were completed. Although
the JAGES respondents were not randomly selected, the data covered a wide range and size ofcommunity populations. This research protocol and informed consent method were approved by the
Ethics Committee of Nihon Fukushi University (No. 13–14).

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Figure 1. Study area.

2.1.2. Selection and Description of the Setting
We used the school district as the neighborhood unit. In Japan, it is common for municipalities tospecify elementary schools and junior high schools according to the student’s address. In other words,
the range of municipalities is divided by multiple school districts. Therefore, this school district wasused as a neighborhood unit. School districts can be used as venues for local festivals, polling stations
for elections, evacuation shelters in the event of a disaster, etc., and can be used as a unit to define thescope of the local community. The school district represents a geographical area that is easy for seniors
to navigate [19]. The average size of a school district was 8.57 km2 (SD = 27.49).
2.2. Outcome Variables
The Geriatric Depression Scale (GDS) [20,21] with its 15 items (score range: 0–15 points) were used
to determine the presence of depression. Those with a total GDS score of 5 or more were considered tohave depression or a tendency towards depression [22,23].
2.3. Types of Greenspace
Using Geographic Information Systems (GIS), all types of greenspaces and a percentage of thetrees, grasslands, and fields were intersected by the school district. This greenspace data were createdbased on the High-Resolution Land-Use and Land-Cover Map (HRM) products by the Japan Aerospace
Exploration Agency (JAXA) and the Earth Observation Research Center (EORC) [24,25]. The resolution
during HRM was 10 × 10 m. The HRM data classifies areas into 10 types of land use: water, urban andbuilt-up, rice paddy, crops, grassland, deciduous tree, evergreen tree, bare land, unclassified, and nodata. In this study, we defined a tree as a covering of a deciduous and evergreen tree. The fieldcomprises a combination of rice paddy and crops. However, we did not change the definition ofgrassland. All kinds of green field were defined as a combination of trees, grassland, and field. For thisanalysis, we calculated the ratio of the area of greenspace in each school district to the area of the school
district. We divided the proportion of each greenspace area into tertiles. Figure 2 shows an example ofHRM data and aerial photographs of the target area. It shows the target areas, which included a city inTokyo, the capital of Japan, and 20 school districts. It also illustrates the urban neighborhoods withmost trees.

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Aerial photographs Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS,
USDA, USGS, AeroGRID, IGN, and the Geographic Information Systems (GIS) User Community.
Figure 2. Example of High-Resolution Land-Use and Land-Cover Map (HRM) data and aerial
Figure 2. Example of High-Resolution Land-Use and Land-Cover Map (HRM) data and aerial
photography of the target area (Hachiouji-city, Tokyo).
photography of the target area (Hachiouji-city, Tokyo).

Aerial photographs Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS,
2.4. Individual-Level Covariates
USDA, USGS, AeroGRID, IGN, and the Geographic Information Systems (GIS) User Community.
The covariates for individuals included sex (male and female), age (65–69, 70–74, 75–79, 80–84,
2.4.
Covariatesattainment (≤9 years, ≥10 years, or missing), annual household income
andIndividual-Level≥85 years), educational(<2 million
Yen, 2–4formillionYen, >4included
million Yen,or missing),familyage
composition(single-living,
2 or
The covariates
individualssex (maleand female),(65–69, 70–74,
75–79, 80–84,
more≥85
living,others,missing),
employment
not working
now,
never hada job,
andyears),
educational
attainment(≤9 (working,years, ≥10retired
years, andor missing),
annualhouseholdincomemissing),
frequency
of
going
out
(<4
times
a
week,
≥4
times
a
week,
or
missing),whetherthey(<2 million Yen, 2–4 million Yen, >4 million Yen, or missing), family composition (single-living,drive
2 orby themselves,the missing),
occupation
with the longest
duration
employment(otheragriculture,moreliving, others,
employment(working,
retiredofand
not working
now,thannever
had a job,
agriculture
forestryof
andfisheries,
fisheries,
hadora missing),
job, or missing),
missing),
frequency
goingout (<4forestry
times a andweek,≥4 timesnevera week,whetherandtheyresidence
drive by
years
(<10
years
and
≥10
years).
Being
able
to
drive
a
car
by
oneself,
andtheoccupation
with thethemselves, the occupation with the longest duration of employment (other than agriculture, agriculture
longest anddurationof forestry
employment
being agriculture
were considered
covariates
becauseforestry
fisheries,
and fisheries,
never had aforestry,job, or missing),
and residence
years (<10yearsmparticipants
with
these
factors
were
more
likelytobeexposedtogreenspace
regardlessoftheand ≥10 years). Being able to drive a car by oneself, and the occupation with the longest durationneighboring
environment.
Othercovariates
were
includedbasedon
previous
studies[22,26].
of employment being agriculture forestry, were considered covariates because mparticipants with
Unknowndata
in morethe dataset
werecategorized
as “missing”
in this analysis.
thesefactors
werelikely to
be exposed
to greenspace
regardlessof the neighboring environment.
Other covariates were included based on previous studies [22,26]. Unknown data in the dataset were
2.5. Neighborhood-Level
Covariates
categorized
as “missing”
in this analysis.
Annual total daylight hours, deepest annual snowfall amount, and annual rainfall were included
2.5. Neighborhood-Level Covariates
as regional covariates. Daylight hours were covariates because they are associated with seasonal
depression
growth[27]deepest
and areannual
relatedsnowfall
to physicalactivity,
with included
snowfallAnnualand
totalgreen
daylight
hours,
amount,and whichannualinteracts
rainfall wereandannual covariates.
rainfall, as wellas green
growthThe abovementioned
covariates
were
surveyedinasregional
Daylight
hours
were[28].
covariates
because they areassociatedwithseasonal
2010 by theand
Ministry
Land, Infrastructure,
Transport,
and Tourism
in Japan, withand they
useddepression
greenofgrowth[27] and are related
to physicalactivity,(MLIT)
which interacts
snowfall
1 kmannualof meshdata issued
inas
2012
[29].
The averagevalueof the specifiedcovariates
regional covariates
for eachandrainfall,as wellgreen
growth[28]. The
abovementioned
were surveyedinschooldistrictwas
calculated
using
GIS.
Additionally,
weconductedapre-stratification
analysisthat2010 by the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) in Japan, and they usedthepopulationof theareas.ofResidential
areas
were definedas building
1considered
km of meshdata
issued indensity
2012 [29].
Theresidential
average value
the specified
regional
covariates
for eachsites indistrict
the land-use
from theusing
MLIT.GIS.
TheAdditionally,
resolution was
× 100 m a[30].
The population
densityofschoolwas calculated
we100
conducted
pre-stratification
analysisthattheresidential
areas
was
calculatedbyequally
distributing
thepopulation
in
the
500
m
mesh
data
considered the population density of the residential areas. Residential areas were defined as building
fromintheof fromthe building
GIS, andthe population
the
sites
thecensus
land-use
the MLIT.site
Theusing
resolution
was by100equally
× 100 mdistributing[30]. The population
densitybyof theneighborhood district polygon.

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residential areas was calculated by equally distributing the population in the 500 m mesh data from thecensus of the building site using GIS, and by equally distributing the population by the neighborhood
district polygon.
2.6. Statical Analysis
We conducted a descriptive analysis to summarize the participants’ characteristics. We also
performed correlation analysis to explore the association between neighborhood-level covariates.
The mean, standard deviation (SD), and minimum and maximum values of each variable at theneighborhood level are shown, along with the results of the Spearman’s ρ test. We then performed
a multi-level logistic regression analysis using random intercepts and a fixed slope to investigate
the association between neighborhood-level greenspace and individual-level depression using a
non-stratified sample, in addition to the stratified analysis by urban and rural characteristics.
The pre-stratification analysis included a total of 126,878 respondents (level 1) nested within the881 neighborhood districts (level 2). The analysis conducted for urban areas included a total of
93,055 respondents (level 1) nested within 710 neighborhood districts (level 2). The analysis conducted
for the rural areas included a total of 33,823 respondents (level 1) nested within 171 neighborhood
districts (level 2). The odds ratio (OR) of depression and 95% confidence intervals (CI) were calculated.
The urban area referred to here, a Large Metropolitan City in an Organization for Economic
Co-operation and Development (OECD) Functional Urban Area (FUA) [31], has a population of morethan 1.5 million. This area is part of a Large Metropolitan City. The rural areas are not covered by
the FUA’s Large Metropolitan City since their population is less than 1.5 million. The OECD FUAs
are considered as internationally comparable cities defined and adopted in 39 countries. The urban
and rural regions were analyzed in two models with different explanatory variables. In Model 1,
the explanatory variables include all greenspace types. In Model 2, the explanatory variables included
trees, grasslands, and fields. Stata MP 14 (Stata Corp, College Station, TX, USA) was used to conduct
the statistical analyses.
3. Results
3.1. Participants’ Characteristics
This study targeted 881 neighborhood districts where individuals were physically and cognitively
independent (i.e., not eligible to receive any benefit from public long-term care insurance), no dataon sex, age, residential area, and depression responses were missing, and there were 50 or more
respondents in a district. We analyzed the data from 126,878 subjects (Figure 3). The average area forthe neighborhood districts was 9.6 km2 (standard deviation [SD] = 5.2 km2 ).
Table 1 shows the characteristics of participants with depression. The prevalence of depression
was 20.4% (n = 25,846). Women constituted 51.5% of the participants, and more than half were under
74 years old. The proportion of participants with an educational attainment of 10 years or morewas 69.7%. The largest proportion based on annual household income was <20,000 dollars (38.5%),
followed 20,000–39,999 dollars (33.3%). The average income in 2014 for older adults households
announced by the Japanese government (households comprising only persons aged 65 years andabove or households in which unmarried persons under the age of 18 are added) was 21,160 dollars,assuming a rate of 100 yen per US dollar [32]. We found that 70.9% of the participants lived withothers, and 26.1% are still working. The average weekly outing frequency was 74.9% for those whowent out more than four times a week. As one of the means of transportation, 58.7% of the participants
drive a car. The type of occupation with the longest duration of employment was agriculture, forestry,
and fisheries (AG). The proportion of participants likely to be exposed to greenspace and bluespaceby occupation was 3.5%. We also showed that 90.4% of the participants have lived in their current
residence for more than 10 years.

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Figure 3. Flow chart of the survey participants.

Table 1. Participants’ descriptive statistics.
Variables

Total(n = 126,878)

Depression(n = 25,846; 20.4%)

No Depression(n = 101,032; 79.6%)

Number%Number%Number%Men

61,49348.5

12,88049.8

48,613

48.1

Women

65,38551.5

12,96650.2

52,419

51.9

65–69

42,150

33.2

8379

32.4

33,771

33.4

70–74

35,398

27.9

6880

26.6

28,518

28.2

75–79

27,928

22.0

5678

22.0

22,250

22.0

80–84

15,058

11.9

3377

13.1

11,681

11.6

≥85

6344

5.0

1532

5.9

4812

4.8

<10

37,736

29.7

9469

36.6

28,26728.0≥1087,866

69.3

16,05662.1

71,81071.1

Missing

1276

1.0

321

1.2

955

0.9

Individual level variables
Sex

Age (years)

Educational attainment (years)

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Table 1. Cont.
Variables

Total(n = 126,878)

Depression(n = 25,846; 20.4%)

No Depression(n = 101,032; 79.6%)

Number%Number%Number%<20,000

48,84338.5

12,51848.4

36,32536.0

20,000–39,999

42,306

33.3

6502

25.2

35,80435.4≥40,000

12,174

9.6

1231

4.8

10,943

10.8

Missing

23,555

18.6

5595

21.6

17,96017.8

No (living alone)

17,802

14.0

5273

20.4

12,52912.4

Yes

89,96970.9

16,67564.573,294

72.5

Others/Missing

19,107

15.1

3898

15.1

15,20915.1

Working

33,158

26.1

5487

21.2

27,671

27.4

Retired and not working now

69,091

54.5

14,60356.5

54,48853.9

Annual household income (Dollars)

Living with others

Employment situation

Never had a job

7590

6.0

1693

6.6

5897

5.8

Missing

17,039

13.4

4063

15.7

12,976

12.8≥4 times

95,05974.9

16,37463.4

78,68577.9<4 times

30,715

24.2

9210

35.6

21,505

21.3

Missing

1104

0.9

262

1.0

842

0.8

No

52,35841.3

12,42148.1

39,93739.5

Yes

74,52058.7

13,425

51.9

61,095

60.5

Other than AG*

102,857

81.1

20,48979.3

82,368

81.5

AG*

4396

3.5

985

3.8

3411

3.4

Never had a job

7192

5.7

1647

6.4

5545

5.5

Missing

12,433

9.8

2725

10.5

9708

9.6

Frequency of going outside (per week)

Drive a car

The longest type of occupation

Residence years<10 years

10,794

8.5

2987

11.6

7807

7.7

≥10 years

114,637

90.4

22,452

86.9

92,185

91.2

Missing

1447

1.1

407

1.6

1040

1.0

AG*: agriculture, forestry and fisheries. Annual household income was reported in Japanese Yen and converted at a
rate of 100 US Dollars per Yen.

3.2. Results of the Neighborhood Level Correlation Analysis
Table 2 shows the Spearman’s correlation coefficients for the neighborhood-level measures.
All greenspace types were correlated with trees, fields, and grasslands (ρ = 0.78, 0.77, 0.70, respectively).
All greenspace types (AG) were inversely correlated with residential population density (ρ = −0.69).
The co-efficient of trees and grasslands was 0.51, while that of fields and grasslands was 0.52.
Our analysis showed that trees, fields, and grasslands were associated. The population density ofresidential areas was strongly inversely associated with AG, fields, and grasslands (−0.688, −0.623,−0.537) and weakly associated with trees (−0.345). This suggests there are areas with many trees,
even in urban areas.

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Table 2. Summary of neighborhood measures and their Spearman associations (n = 881).
Neighborhood Measures

Mean

SD

Median

Min

Max

1

2

3

4

37.3

32.2

1.1

0.0

99.8

1.000

15.5

22.7

0.8

0.0

98.7

0.781 **

1.000

20.1

20.6

0.7

0.0

91.6

0.766 **

0.389 **

1.000

1.7

3.1

0.1

0.0

39.7

0.696 **

0.510 **

0.520 **

1.000

9550.95166.6

174.1

795.8

37915.6 −0.688 **−0.345 **−0.623 **−0.537 **Total (n = 881)
1. All kinds of green field(area ratio)
2. Trees(area ratio)
3. Fields(area ratio)
4. Grasslands(area ratio)
5. Residential population
density (population/km2 )** p < 0.01. SD, standard deviation; Min, minimum; max, maximum.

3.3. Results of the Pre-Stratification Analysis
The main results of multi-level logistic regression analysis for the pre-stratification are shownin Figure 4, and the details are shown in Table 3. According to Model 1 in Table 3, the highest
tertile of all the greenspace areas had a lower oddof depression than their respective reference groups(OR 0.90, 95% CI 0.85–0.95). According to Model 2, the highest and middle tertiles of trees had
lower odds of depression than their respective reference groups (OR 0.93, 95% CI 0.88–0.99; and OR
0.92, 95% CI 0.88–0.96, respectively). No statistically significant associations were observed between
grasslands, fields, and depression.
Table 3. Odds ratios (ORs) with 95% confidence intervals (95% CI) for the association of greenspaceand depression among older Japanese adults (n = 126,878).
Variables

Model 1Model 2OR (95% CI)

OR (95% CI)

NumberAll types of greenspaces (area ratio)
Lowest tertile (0–25.66)

42,500

1.00

Middle tertile (25.66–70.86)

41,9100.97(0.93–1.02)

Highest tertile (≥70.86)

42,4680.90(0.85–0.95) *Trees (area ratio)
Lowest tertile (0–2.00)

42,341

1.00

Middle tertile (2.00–16.96)

42,2300.92(0.88–0.96) *Highest tertile (16.97–98.69)

42,3070.93(0.88–0.99) *Grasslands (area ratio)
Lowest tertile (0–0.25)

42,266

1.00

Middle tertile (0.25–2.06)

42,2310.98(0.93–1.03)

Highest tertile (2.07–39.7)

42,381

0.97(0.92–1.03)

Fields (area ratio)
Lowest tertile (<13.38)

42,340

1.00

Middle tertile (13.38–36.11)

42,4941.01(0.96–1.06)

Highest tertile (≥36.12)

42,0441.01(0.95–1.07)

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Table 3. Cont.
Variables

Model 1Model 2OR (95% CI)

OR (95% CI)

NumberResidential population density (persons per kilometer squared)
Lowest tertile (795.6–4508.5)

42,2561.00

1.00

Middle tertile (4508.5–8957.5)

42,2860.94(0.89–1.00) *0.97(0.92–1.02)

Highest tertile (8957.5–37,915.6)

42,3360.89(0.84–0.95) *0.94(0.88–1.00) ** p < 0.05. The random-effect estimate (standard error) of the urban areas’ null model was 0.03 (0.001).
Unknown data of variables in the dataset were categorized as missing in this analysis. All the models were
adjusted for individual-level variables (sex, age, educational attainment, annual household income, living withothers, employment situation, frequency of going outside, driving a car, residence duration in years, the longest
type of occupation) and neighborhood-level variables (annual total daylight, deepest annual snowfall amount,annual rainfall, residential population density).

Figure 4. Odds ratios (ORs) with 95% confidence intervals (95% CI) for the association of all kinds ofgreenspace and trees with depression among older Japanese adults (n = 126,878).

3.4. Results of the Urban Area Analysis
The main results of multi-level logistic regression analysis for the pre-stratification are shownin Figure 5, and the details are shown in Table 4. Table 4 shows the results of the multi-level logistic
regression analysis, stratified by urban and rural. In urban areas, 18,474 (19.9%) people had depressive
symptoms. In Model 1, the highest tertile of all greenspace areas had a lower oddof depression thantheir respective reference group (OR 0.96, 95% CI 0.91–1.00). In Model 2, the highest and middle
tertiles for trees showed lower odds of depression than their respective reference group (OR 0.94,
95% CI 0.89–1.00; and OR 0.95, 95% CI 0.90–1.00, respectively). No statistically significant associations
were observed between grasslands, fields, and depression.

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Table 4. Odds ratios (ORs) with 95% confidence intervals (95% CIs) for the association of greenspaceand depression among older Japanese adults.
Urban (n = 93,055)
Variables

Rural (n = 33,823)

Model 1Model 2Model 1Model 2OR (95% CI)

OR (95% CI)

OR (95% CI)

OR (95% CI)

All types of greenspaces (area ratio)
Lowest tertile

1.00

1.00

Middle tertile

0.97(0.93–1.02)

0.97(0.88–1.07)

Highest tertile

0.96(0.91–1.00)

0.94(0.85–1.03)

Trees (area ratio)
Lowest tertile

1.00

1.00

Middle tertile

0.95(0.90–1.00) *0.93(0.84–1.02)

Highest tertile

0.94(0.89–1.00) *1.00(0.89–1.13)

Lowest tertile

1.00

1.00

Middle tertile

1.01(0.96–1.06)

0.91(0.83–1.00) *Highest tertile

1.00(0.94–1.06)

0.92(0.82–1.02)

Lowest tertile

1.00

1.00

Middle tertile

1.02(0.97–1.08)

1.06(0.95–1.17)

Highest tertile

1.03(0.96–1.09)

1.14(1.01–1.28)

Grasslands (area ratio)

Fields (area ratio)* p < 0.05. The random-effect estimates (standard error) of the urban and rural areas’ null models were0.03 (0.01) and 0.02 (0.01), respectively. All the models were adjusted for individual-level variables (sex, age,educational attainment, annual household income, living with others, employment situation, frequency of goingoutside, driving a car, residence duration in years, the longest type of occupation), and neighborhood-level variables(total daylight hours, deepest annual snowfall amount, annual rainfall). Unknown data of variables in the dataset
were categorized as missing in this analysis. The cutoff (tertiles 1 to 3) of urban’s greenspace tertiles for all
types of greenspaces (<18.1, 18.1–52.8, and ≥52.8), trees (<1.5, 1.5–11.9, and ≥11.9), grasslands (<0.1, 0.1–1.4,
and ≥1.4), and fields (<13.4, 13.4–36.1, and ≥36.1) are shown. The cutoffs (tertiles, lowest to highest) for therural areas’ greenspace tertiles for all types of greenspaces (<71.0, 71.0–92.6, and ≥92.6), trees (<6.0, 6.0–55.4,
and ≥55.4), grasslands (<1.1, 1.1–4.8, and ≥4.8), and fields (<13.4, 13.4–36.1, and ≥36.1) are shown. The numbers(tertiles lowest to highest) for urban areas’ greenspace tertiles for all types of greenspaces (11,189, 11,818, and 10,816),
trees (11,289, 11,199, and 11,335), grasslands (11,364, 11,230, and 11,229), and fields (6126, 11,106, and 16,591)
are shown. The numbers (tertiles, lowest to highest) for rural areas’ greenspace tertiles for all kinds of greenspaces(29,821, 32,396, and 30,838), trees (30,630, 31,437, and 30,988), grasslands (31,015, 31,275, and 30,765), and fields(36,214, 31,388, and 25,453) are shown.

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Figure 5. Odds ratios (ORs) with 95% confidence intervals (95% CI) for the association of all kinds ofgreenspace and trees with depression among older adults in urban areas (n = 93,055).

3.5. Results of the Rural Area Analysis
The main results of multi-level logistic regression analysis for the pre-stratification are shownin Figure 6, and the details are shown in Table 4. As shown in Table 4, in rural areas, 7372 (21.8%)
people were reported to have depressive symptoms. In Model 1, no statistically significant association
was observed between all types of greenspaces and depression. According to Model 2, the middle
tertile of grasslands had a lower oddof depression than their respective reference group (OR 0.91,
95% CI 0.83–1.00). No statistically significant associations were observed between trees, fields,
and depression.

Figure 6. Odds ratios (ORs) with 95% confidence intervals (95% CI) for the association of all kinds ofgreenspace and grasslands with depression among older adults in rural areas (n = 33,823).

4. Discussion
To the best of our knowledge, this is the first study to demonstrate the contextual relationshipbetween types of neighborhood greenspaces and depression in independent older adults. This study
also adds to the evidence reported by previous studies by revealing that the association between
the types of greenspaces and depression differ between urban and rural areas. The results of thisstudy are supported by the relationship of greenspaces with good mental health reported in previous
studies [3,5]. With regard to the difference in the types of greenspaces, we found that more trees wereassociated with lower odds of depression; however, this was not the case for grasslands and fields

Int. J. Environ. Res. Public Health 2020, 17, 9276

12 of 164.1. Results from the Urban Stratified Analysis
In urban areas, higher rate of tree areas was associated with lower odds of depression. This finding
is similar to previous research on street-view green [33] and urban green types [8] of street-view.
Urban trees are considered to have a superior effect in reducing the heat island phenomenon,
mitigating noise, and improving the air quality compared to grasslands and fields [3]. This is because
the trees have more greenness than the grasslands and fields in the same area; hence, the green effectmay be greater. It is also thought that trees are more resistant to changes in weather and seasons
than plants in fields and grasslands and have a positive effect on the environment over a long period.
These positive effects of trees on the neighborhood are likely to encourage older adults to go out andreduce stress. It is known that having more trees in urban areas is related to the length of walking
distance of older adults [9] and physical activities such as walking are associated with a lower rateof depression [2].
Urban trees improve subjective health [8]. Furthermore, greenspaces in an artificial environment
may attract people’s attention and enhance their impression according to the Attention Restoration
Theory [34,35]. Therefore, in urban areas, the presence of trees may have influenced the reduction in theodds of depression through the effects of greenspace on stress. For the Japanese people, Shinrin Yoku
(forest bathing) is an ancient cultural tradition [36]. It has long been said that bathing in the power oftrees is good for health. It has been reported that exposure to a magnificent view of nature, which people
in urban areas lack, and nature therapy (e.g., forest bathing) are beneficial for the mental health of
those who live in urban areas and are not engaged in occupations associated with agriculture, forestry,or fisheries [37]. According to the attention restoration theory, moving from an environment with fewer
greenspaces to one with more greenspaces may gradually amplify the positive effects of greenspace on
mental health.
4.2. Results from the Rural Stratified Analysis
In rural areas, all types of greenspaces and trees were not significantly associated with depression.
These findings are similar to those from a previous cross-sectional study, in which the types ofgreenspaces were not associated with the subjective health in rural areas [38]. In contrast, a moderate
rate of grassland areas was associated with lower odds of depression. Grasslands show a wider fieldof view, easier access, and cause less anxiety than trees. They may also be used as places for physicalactivity and socialization. Opening the field of vision to natural sights (e.g., landscapes and views)
is related to social cohesion and mutual cooperation; thus, it is likely related to a lower incidence
of depression [6].
Older adults in areas with more grassland may not show association with depression because
they had less visibility and access to grasslands than in areas with moderate amounts, making themless accessible for physical activity and socialization. A previous study assessing the relationshipbetween tree density and stress reported that moderate amounts of trees contribute to stress recovery,
while large amounts of trees weaken stress recovery. For men, the dose–response curve was aninverted-U shape as tree cover density increased [39]. In the suburbs, there are too many trees in aneighborhood, which delays stress relief and may not be associated with less depression. Similar effects
can be expected in areas with a lot of grasslands.
4.3. Features of the Analysis Method Used in This Study
This study included different methodological features compared to previous studies. First, we focusedon the types of greenspaces. Several studies that analyzed the relationship between greenspace andhealth quantified the greenspace from satellite images in the form of the Normalized Difference
Vegetation Index (NDVI). The NDVI can be used to identify the density of greenness per unit area but
cannot distinguish the types of greenspaces. In addition, due to the characteristics of the data, it wasdifficult to prepare data that covers a wide area as in this study, for the same period.

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To compensate for this disadvantage, a combination of Light Detection and Ranging (LiDAR)
data, a three-dimensional laser survey, which enables researchers to distinguish between the types ofgreenspaces such as trees, shrub, and grass using height data, exists [40]. LiDAR data, with its large
amount of data and cost of imaging, are more suited for comparing small cities, but it is difficult to use
for large-scale surveys targeting dozens of municipalities, such as in this study. Therefore, in our study,
the types of greenspaces were evaluated using the JAXA data that classified the types of greenspaces
from satellite images, in response to a large-scale survey. Secondly, our study differs from prior work
based on the analyzed regional units. Previous studies evaluated the greenspaces in buffers fromaddresses used as representative points [8,9,41,42]. The buffers used here are often in the range of
300 to 3000 m. Thus, the greenspace was analyzed previously at an individual level. In this study,
in order to evaluate the impact at the neighborhood level, we conducted a multi-level analysis of school
districts. This study showed similar results with those of previous studies, despite the differences
outlined above.
4.4. Limitations
Several limitations of this study should be mentioned. First, this is a cross-sectional study whichcannot reveal the effect of reverse causation. Secondly, as the data is based on a questionnaire, recall bias
may have occurred. Thirdly is the calculation of the amount of greenspace. The data used in this report
indicate the types of greenspaces for each mesh but not the density of the greenness, unlike that of the
NDVI. Moreover, the amount of greenspace exposure according to each person’s behavior was not clear.
In the future, studies considering these limitations will be required, using quantitative data, such as
NDVI and GPS. Furthermore, it is necessary to analyze the amount of greenspace, including trees,
occurring at the eye level on the basis of the local characteristics (e.g., urban/rural) and their appearance
as natural sights in order to improve the physical environment and develop new architecture andcity plans.
5. Conclusions
In conclusion, we clarified that the types and amounts of greenspaces showed different associations
with the degree of depression in older adults in Japan depending on the regional (whether urban orrural) characteristics. Lower odds of depression were associated with a large amount of greenspaceamong Japanese older adults. Urban areas with an abundance of trees and rural areas with moderateamounts of grassland were associated with lower odds of depression.
In the future, our findings could be effective as guidelines for primordial prevention throughthe development of planned greenspaces according to the regional characteristics. These findings
may be useful in policymaking for urban and architectural planners to determine the need for securegreenspaces, as well as the types of greenspaces that should be maintained in urban and rural areas.
Author Contributions: M.N.: conceived the design, analyzed the data, performed the literature review, and wrote
the first draft of the article. C.K., M.H.: designed and critically revised the article. K.K. designed and criticallyrevised the article and is the principal investigator for the JAGES project. All authors have read and agreed to the
published version of the manuscript.
Funding: This study used data from JAGES (the Japan Gerontological Evaluation Study), JSPS (Japan Society
for the Promotion of Science) KAKENHI grant numbers (JP15H01972, JP15H04781, JP15H05059, JP15K03417,
JP15K03982, JP15K16181, JP15K17232, JP15K18174, JP15K19241, JP15K21266, JP15KT0007, JP15KT0097, JP16H05556,
JP16K09122, JP16K00913, JP16K02025, JP16K12964, JP16K13443, JP16K16295, JP16K16595, JP16K16633, JP16K17256,
JP16K19247, JP16K19267, JP16K21461, JP16K21465, JP16KT0014, 19K04785, 20H00557, JP25253052, JP25713027,
JP26285138, JP26460828, JP26780328 and 20K13721), Health Labor Sciences Research Grants (H26-Choju-Ippan-006,
H27-Ninchisyou-Ippan-001 H28- Choju-Ippan-002, H28- Ninchisyou-Ippan-002, 19FA1012, 19FA2001) from the
Ministry of Health, Labor, and Welfare, the Research and Development Grants for Longevity Science from AMED
(Japan Agency for Medical Research and development) (16dk0110017h0002, 16ls0110002h0001), the Research
Funding for Longevity Sciences from the National Center for Geriatrics and Gerontology (24-17, 24-23), the JapanFoundation For Aging And Health, and a JST-OPERA program grant (JPMJOP1831).

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Acknowledgments: This study used data from JAGES, conducted by the Nihon Fu-kushi University Center for
Well-Being and Society, for their research projects. We are extremely grateful to all study participants for the use of
their personal data. We would like to express our deepest gratitude to everyone who participated and cooperated
in the surveys.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of thestudy; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.

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