(2021) 20:42
Kanamori et al. Int J Health Geogr
https://doi.org/10.1186/s12942-021-00296-8International Journal of
Health Geographics
Open Access

RESEARCH

Differences in depressive symptoms
by rurality in Japan: a cross‑sectional multilevelstudy using different aggregation unitsof municipalities and neighborhoods (JAGES)
Mariko Kanamori1,2, Masamichi Hanazato3,6, Daisuke Takagi1, Katsunori Kondo3,4, Toshiyuki Ojima5,
Airi Amemiya2 and Naoki Kondo2*

Abstract
Background: Rurality can reflect many aspects of the community, including community characteristics that may be
associated with mental health. In this study, we focused on geographical units to address multiple layers of a rural
environment. By evaluating rurality at both the municipality and neighborhood (i.e., a smaller unit within a municipality) levels in Japan, we aimed to elucidate the relationship between depression and rurality. To explore the mechanisms linking rurality and depression, we examined how the association between rurality and depression can be
explained by community social capital according to geographical units.
Methods: We used cross-sectional data from the 2016 wave of the Japan Gerontological Evaluation Study involving
144,822 respondents aged 65 years or older residing in 937 neighborhoods across 39 municipalities. The population
density quintile for municipality-level rurality and the quintile for the time required to reach densely inhabited districts
for neighborhood-level rurality were used. We calculated the prevalence ratios of depressive symptoms by genderusing a three-level (individual, neighborhood, and municipality) Poisson regression. Community social capital wasassessed using three components: civic participation, social cohesion, and reciprocity.
Results: The prevalence of depressive symptoms was higher in municipalities with lower population density thanthose with the highest population density; the ratios were 1.22 (95% confidence intervals: 1.15, 1.30) for men and 1.22
(1.13, 1.31) for women. In contrast, when evaluating rurality at the neighborhood level, the prevalence of depressive
symptoms was 0.9 times lower for men in rural areas; no such association was observed for women. In rural municipalities, community civic participation was associated with an increased risk of depressive symptoms. In rural neighborhoods, community social cohesion and reciprocity were linked to a lower risk of depressive symptoms.
Conclusions: The association between rurality and depression varied according to geographical unit. In ruralmunicipalities, the risk of depression may be higher for both men and women, and the presence of an environment
conducive to civic participation may contribute to a higher risk of depression, as observed in this study. The risk of*Correspondence: kondo.naoki.0s@kyoto-u.ac.jp
2
Department of Social Epidemiology, Graduate School of Medicine,
Science Frontier Laboratory, Kyoto University, Floor 2, Yoshida‑konoe‑cho,
Sakyo‑ku, Kyotoshi, Kyoto, Japan
Full list of author information is available at the end of the article© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Kanamori et al. Int J Health Geogr(2021) 20:42

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depression in men may be lower in rural neighborhoods in Japan, which may be related to high social cohesion andreciprocity.
Keywords: Depressive symptoms, Geographical unit, Japan, Multilevel analysis, Municipality, Neighborhood, Olderadults, Rural, Social capital, Urban

Background
Suicide is an important global public health issue that
results in approximately 703,000 deaths annually [1].
Depression is recognized as an important risk factor
for suicide [2, 3]. It is also a risk factor for physical diseases, such as cardiovascular disease and diabetes, andis the leading global cause of disability [4]. Both suicideand depression have been reported to be associated withrurality or urbanicity, but the directions of the associations are not consistent among studies. Some studies
from high-income countries have shown that depression is more prevalent in urban areas than in rural areas,
whereas no such association has been observed for lowand middle-income countries [5–8]. In addition, suicide rates have shown to be higher in rural areas than inurban areas in various countries [9–11]. The differencesin findings across studies may be due to the differencesin the processes of the onset of the two outcomes andthe measurements of the health outcomes and of rurality/urbanicity (hereinafter referred to as rurality forsimplicity).
A focus on geographical units to evaluate rurality may
be beneficial for understanding the mechanisms linking the characteristics of residence and mental health.
Rurality is an important area-level factor that encompasses many aspects of a community [12]. The measuresof rurality observed at different levels of aggregation canreflect different contextual features, and different putative mechanisms may be implicated [13–15]. For example, given that a municipality is the smallest definable
political/administrative unit, disadvantages observedin rural municipalities in terms of public service policies and other specific conditions may be associatedwith poor mental health-related outcomes. Because themunicipal population can be expected to contributeto municipal tax revenue and, in turn, the amount andquality of public service supply and human resources,
municipality-level rurality in terms of underpopulation
may reflect these varying material environments as aconsequence of political decisions made by the municipal
government. However, even if the population is large, ifthey are spread out over a large area of land, that is, if the
population density is low, then the efficiency of the use of
public resources will be low.
In addition to the overall population density of themunicipality, geographical variations in population

density within the municipality should also be considered. Highly populated central areas in a municipality,referred to as densely inhabited districts (DIDs), often
have a high concentration of community functions necessary for daily life. In contrast, residents of neighborhoods that are far from DIDs within a municipality suffer
more from the disadvantages of rural contexts, including
limited access to public resources, such as public transportation and opportunities for social activities (e.g., job
and group activities). Rural neighborhoods, that is, communities distant from DIDs, can also be expected to haveother characteristics. First, rural neighborhoods are likely
to base their economic activities more on primary industries (e.g., farming) rather than secondary and tertiaryindustries. Second, rural neighborhoods may have specific functions in their community social capital, including the senses of mutual trust and reciprocity, related tothe types of communal farming activities and the needto help one another in daily life [16]. Hence, neighborhood rurality may be associated with mental health independent of the rurality of the municipality to which theneighborhood belongs, and rurality in a municipality andneighborhood may influence residential mental health indifferent ways. In one cohort study involving all adults in
Sweden, municipality-level rurality was associated withsuicide rates in men, and individual socioeconomic status
explained the excess risk of suicide in rural municipalities but not at the neighborhood level [17]. However, tothe best of our knowledge, no study has investigated theassociations between rurality and depression when evaluating rurality by community size (i.e., municipalities andneighborhoods).
The urban/rural differences in the prevalence of depression among Asian countries are inconsistent. Reports
from China and Myanmar [18] showed a higher prevalence of depression in rural areas than in urban areas,
while reports from India [19], South Korea [20], Taiwan[21], and Vietnam [22] showed the opposite or no statistically significant differences. Investigation of the rural/
urban gap on depression prevalence in Japan has been
limited to those conducted in a single prefecture [23,24]. Studies that have evaluated the differences in suicide rates in Japan among rural and urban settings have
had an ecological design at the prefectural or municipal
level [25–29]. Because of the limitations of such a study
design, it was not possible to analyze individual andKanamori et al. Int J Health Geogr(2021) 20:42

regional effects separately or discuss them separately at
the municipality level and link small regional-level factors to mental health-related outcomes.
With this in mind, our study was designed to evaluaterurality at both the municipality and neighborhood (i.e., asmaller unit within a municipality) levels in Japan (Fig. 1).
In Japan, the most aged society in the world, there aremany farming neighborhoods located in mountainous
areas that are far from the DIDs. In addition, to explore
the potential differential mechanisms that link rurality
to depression by aggregation unit, we focused on community social capital, a factor that has not been widely
investigated in relation to urban–rural differences in
depression. To achieve this, we examined how the association between rurality and depression can be explainedby community social capital according to geographicalunits.

Methods
Data collection

Cross-sectional data were obtained from the 2016 waveof the Japan Gerontological Evaluation Study (JAGES), alarge-scale, population-based collaborative study of voluntarily participating municipalities. The data collection
period spanned October 3, 2016, to December 5, 2016,
inclusive, with an additional period of data collection in
January 2017 for approximately 2000 people residing in a
municipality. The JAGES involved the distribution of ananonymous self-administered mail survey in cooperation
with different municipalities across Japan. The questionnaire was sent to residents in participating municipalities
aged 65 years or older who were not certified as needing public long-term care insurance. The 2016 wave
consisted of individuals living in 39 municipalities inPage 3 of 13

18 prefectures out of the 47 total prefectures in Japan,
including the northernmost prefecture (Hokkaido) andthe Kyusyu region (Kumamoto prefecture) in the southern area. Municipalities of various population sizes areincluded, ranging from approximately 1000 to 3.7 million
people. The proportion of older adults ranged from 20.5%
to 50.4% (the overall proportion of older adults in Japanwas 27.3% in 2016 [30]). A map of the study area is available on the JAGES website [31].
In the larger municipalities (n = 22), the participants
were randomly selected by a multistage sampling process,while in the smaller municipalities (n = 17), all eligible
individuals were included. In total, 279,661 questionnaires were mailed to potential participants, and 196,431
were returned, which equated to a response rate of 70.2%.
However, this figure inadvertently included some people certified as needing public long-term care insurance;
after excluding these individuals, data for 180,021 individuals were available for analysis. The data have a threestage hierarchical structure: individuals, school districts(neighborhoods), and municipalities. In Japan’s administrative classification, municipalities (shi, cho/machi, or
son/mura) are the smallest local public entities and are
positioned a level below prefectures (to, do, fu, or ken).
To evaluate rurality at the municipality level, we
obtained data on municipal population density from the
population census. To evaluate rurality at the neighborhood level, we collected data on the time required toreach a DID using the government census of agriculture
and forestry for 2015 obtained from the website of the
Ministry of Agriculture, Forestry, and Fisheries [32]. We
used these data to ensure the proportional distribution ofdata in each JAGES district.
Measurements
Outcome: depressive symptoms

We used the Japanese short version of the GeriatricDepression Scale (GDS-15) to evaluate depressive symptoms in our cohort. The GDS-15 was developed to assess
depressive symptoms in self-administered surveys andconsists of 15 items with binary yes (scored 1)/no (scored0) answer options, resulting in a total score ranging from0 to 15, with higher scores indicating more depressive
symptoms. In this study, we used a score of 6 or more tocategorize depressive symptoms, which was used to indicate moderate symptoms in an earlier validation study
performed on older Japanese adults [33] and has beenreported to be highly associated with suicidal ideation[34].
Fig. 1 Conceptual figure showing the definitions of rurality/
urbanicity by two regional levels in this study. Rurality was assessedat both the municipality and the neighborhood (i.e., a smaller unit
within a municipality) levels

Rurality

Because there is no universal definition of rurality, which
can include multiple dimensions, such as geographical,

Kanamori et al. Int J Health Geogr(2021) 20:42

political, and cultural characteristics [35], defining rurality according to the study purpose is recommended [36].
In this study, we adopted the following two variables todefine rurality: population density quintile (for municipality-level rurality) and quintile of the time required toreach the DID (for neighborhood-level rurality; Fig. 2).
The latter variable was defined as the amount of time ittakes to travel from the center of a rural community to
the center of a DID by the transportation mode (e.g., car,bus, or train) typically used by residents. A study thatcompared multiple rurality/urbanicity indicators foundthat population density and accessibility (e.g., number ofpeople that can be reached within a certain travel time)
were the most sensitive proxy indicators for identifying
the relationship between rurality and suicide [37].
Community social capital

We used the health-related community social capital scale [38] to assess three components of community
social capital (i.e., levels of community civic participation, social cohesion, and reciprocity) at the neighborhood level. The level of community civic participation
was quantified by summing each individual’s participation in any three types of community groups (volunteer
groups, sports groups, and hobbies) or activities in which
the frequency of participation was once a month or more.
Levels of social cohesion in the community were measured by summing the percentage of those who answered“very” or “moderately” to three HR-CSC items: trust (“Doyou think that people living in your area can be trusted ingeneral?”), perceptions of others’ intentions to help (“Doyou think that people living in your area try to help others in most situations?”), and attachment to the residential area (“How attached are you to the area in which you
live?”); other potential answers to these items included“neutral,” “slightly,” and “not at all.” The third component,
levels of community reciprocity, was measured by summing the percentage of individuals who received emotional support (“Is there someone who listens to yourconcerns and complaints?”), provided emotional support(“Do you listen to others’ concerns and complaints?”), or
received instrumental support (“Is there someone wholooks after you when you are sick and confined to bed
for a few days?”). The levels of community civic participation, community social cohesion, and community reciprocity were standardized in our cohort [38].
Covariates

To improve the robustness of our statistical analysis, we
adjusted the data from participants according to theirage groups: 65–74 years, 75–84 years, and ≥ 85 years.
As individual socioeconomic status may be both a confounder and a mediator in the relationship between

Page 4 of 13

rurality and depression, we conducted additional analyses adjusting for individual sociodemographic status
as follows: years of education (< 9 years or not, which isequivalent to graduating from junior high school), equalized household income tertiles (high, middle, or low),marital status (having a spouse or not), and living alone
or not [39–43].
Statistical analyses

A total of 35,199 respondents were excluded from ouranalyses for the following reasons: missing valid values
for age (n = 262), gender (n = 30), and residential address(n = 7); living in a very small neighborhood of 50 residents or less (n = 3,255; this was an exclusion criterion
to remove neighborhoods with large standard errors inarea-level social capital measurements); and a lack ofoutcome data (n = 31,645). We used a three-level (individual, neighborhood, and municipality) Poisson regression to calculate the prevalence ratios for depressive
symptoms. To avoid overestimation of the calculated
prevalence ratios, a Poisson regression with robust variance was used to model depressive symptoms, which wasregarded as a frequently occurring outcome [44]. We
stratified all of the performed analyses by gender because
a previous study that examined the relationship between
rurality and suicide found different associations for men
and women [45]. Statistical analyses were performed
using STATA [46] including the mepoisson command in
STATA/MP 15.1. We incorporated random intercepts
at the neighborhood and municipality levels into theanalyses.
We could not find a statistical package that could conduct multiple imputations using a three-level model;
therefore, we created dummy variables for the missing
covariate data and modeled them. First, we analyzed anull (empty) model to evaluate the variance of the random parameters. We then created Model 1, which incorporated municipality-level rurality, neighborhood-level
rurality, and age, followed by Models 2–4, which incorporated the variables of Model 1 as well as community
social capital (civic participation, social trust, and reciprocity). To evaluate the general contextual effects inour data, we calculated the median rate ratios from theestimated variance of the random parameters, which represented the median relative change in the prevalenceof depressive symptoms when comparing two randomly
selected residents chosen from two different groups: one
with a higher prevalence of depressive symptoms and theother with a lower prevalence of depressive symptoms[47].
To assess the differential effects of neighborhood-level
rurality and municipality-level rurality, we tested theinteraction term between them (adjusted for age). To

Kanamori et al. Int J Health Geogr(2021) 20:42

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Fig. 2 Municipal population density and densely inhabited districts. Municipalities that participated in the study from Chita Peninsula, which isamong the study areas, are shown as examples

Kanamori et al. Int J Health Geogr(2021) 20:42

Page 6 of 13

perform a sensitivity analysis, rather than using the above
rurality measure, we used municipal population density
centered on the grand mean as municipality-level rurality and population density centered within the cluster forneighborhood-level rurality.

Results
In total, data from 69,660 men and 75,162 women living within 937 neighborhoods across 39 municipalities
were analyzed. As shown in Table 1, depression was moreprevalent in areas with lower population densities among
both men and women at the municipal level. In contrast, there was no marked difference in the prevalenceof depressive symptoms according to neighborhood-level
rurality. Table 2 shows that with respect to community
civic participation at the municipality level, lower population density was associated with less citizen participation(mean: 0.86–0.60), while at the neighborhood level, there
was slightly lower citizen participation in the school districts that took the longest time to reach a DID. However,this effect was rarely seen at the municipality level (mean:
0.81–0.72). Furthermore, Table 2 also shows that at boththe municipality and neighborhood levels, rural areas
were associated with higher levels of social cohesion. As
shown in Additional file 5: Fig. S1, in rural municipalities, there were more districts that took a longer time toreach DID and had less community civic participation.
In addition, compared to rural municipalities, urban

municipalities had more social participation overall,
regardless of the rurality of the school district.
The null model of the multilevel Poisson regression
analysis confirmed that the prevalence ratio of depressive symptoms varied slightly between municipalities andneighborhoods. The median rate ratios for municipalities
and neighborhoods were 1.13 and 1.04 in men and 1.16
and 1.01 in women, respectively (Tables 3, 4). When only
neighborhood-level rurality and age were included in themodel, the median rate ratio was almost unchanged (datanot shown), but when municipality-level rurality wasadded, the median rate ratio for municipalities decreased
to 1.10 in men and 1.11 in women. The variances in
depressive symptoms among municipalities and amongneighborhoods were partially explained by civic participation (Model 2).
Among men, the prevalence of depressive symptoms
was approximately 1.2 times higher in municipalities withlower population density than in municipalities with thehighest population density (Model 1, Table 3). The prevalence of depressive symptoms in men was 0.90 times
lower in neighborhoods with a longer time required toreach the DID than in neighborhoods with the shortest
time required (Model 1, Table 3). Low levels of civic participation in rural municipalities explained their excess
risk of depression (Model 2). High levels of social cohesion and reciprocity in rural neighborhoods were linked
to a lower risk of depressive symptoms (Models 3 and 4).

Table 1 Descriptive statistics of the variables included in the study
Men

Women

Participants (n)Prevalence of depressive
symptoms (%)

Participants (n)Prevalenceof depressive
symptoms (%)

65–74

41,26516.3

44,033

15.2

75–84

24,26818.3

26,502

17.9

≥ 85

4127

22.6

4627

24.5

Highest15,61016.4

16,18015.5

High13,661

18.3

15,04317.0

Middle16,18816.1

17,02314.8

Low

12,04518.9

13,17918.5

Lowest12,156

17.9

13,737

18.5

AgePopulation density (indicator for municipality-level rurality)

Time to reach the DID (indicator for neighborhood-level rurality)
Shortest

13,74018.0

15,080

16.8

Short

11,00317.7

11,931

15.8

Middle16,832

17.3

18,032

16.6

Long

14,60316.8

15,291

16.4

Longest13,482

17.2

14,82817.8

Kanamori et al. Int J Health Geogr(2021) 20:42

Page 7 of 13

Table 2 Descriptive statistics of community social capital (civic participation, social cohesion, and reciprocity) by rurality
Number of school
districts

Civic ­participationa

Social ­cohesiona

Reciprocitya

Mean

(SD)

Mean

(SD)

Mean

(SD)

Highest394

0.86(0.16)

1.56(0.15)

1.97(0.049)

High

259

0.81

(0.15)

1.53(0.15)

1.96(0.051)

Middle87

0.82(0.13)

1.63(0.11)

2.00(0.023)

Low

116

0.72

(0.15)

1.63(0.12)

1.99(0.038)Lowest81

0.60(0.16)

1.67(0.12)

1.99(0.033)

Shortest

349

0.81

(0.16)

1.52(0.15)

1.96(0.051)

Short

125

0.84

(0.14)

1.59(0.12)

1.98(0.038)Middle185

0.83(0.16)

1.58(0.15)

1.98(0.051)

Long

140

0.80

(0.18)

1.61(0.12)

1.99(0.034)Longest138

0.72(0.21)

1.68(0.13)

1.99(0.037)

Population density(municipality-level rurality)

Time to reach the DID(neighborhood-level
rurality)

SD standard deviationa

Evaluated in school districts. Unstandardized value

In women, the prevalence of depressive symptoms
was approximately 1.2 times higher in municipalities
with lower population density than in municipalities
with the highest population density (Model 1, Table 4).
The prevalence of depressive symptoms in women did
not vary according to neighborhood rurality (Model 1,
Table 4), but civic participation explained the excess riskof depression in rural municipalities (Model 2).
Individual sociodemographic status also partly
explained the high risk of depressive symptoms in ruralmunicipalities but did not explain the differences inneighborhoods (Additional file 1: Table S1). Our sensitivity analyses showed that the results of the regression
models using alternative rurality measures were mostly
similar to those of our original models (Additional files 2,
3: Table S2, S3). No consistent pattern was observed forthe interaction between municipality-level rurality andneighborhood-level rurality (Additional file 6: Fig. S2).

Discussion
Our data indicate that the prevalence of depressive symptoms is about 1.2 times higher for both genders in ruralmunicipalities. The excess risk of depressive symptoms
in rural municipalities is partly explained by neighborhood-level civic participation but not by other social
capital domains; this accounted for 3–4% of the variancein depressive symptoms among municipalities and 1–3%of the variance among neighborhoods. In contrast, at theneighborhood level, compared to urban neighborhoods,
the prevalence of depressive symptoms was 0.9 times

lower for men in rural neighborhoods, whereas no such
association was observed for women. The lower risk ispartly explained by neighborhood-level social cohesion
and neighborhood-level reciprocity but not by neighborhood-level civic participation.
The differences in the association between rurality anddepression and the mechanisms underlying the variation across geographical units have received little attention among studies of the urban–rural depression gap.
A study performed in Ghana and South Africa found no
substantial urban–rural differences in the strength of theassociation between social capital and depression [48]. Ina report from China that focused on the impact of rapid
urbanization on social capital and health, the association
between social capital and depression varied by period
and rurality [49]. Psychological variables, including social
capital, social capital satisfaction, and self-esteem, also
play a role in depression among the elderly, as shown by
Korea Welfare Panel Study data [50] as well as data fromthe China Family Panel Studies [51].
Community civic participation assessed at the neighborhood level partly explained the high level of depression in rural municipalities. The presence of a social andmaterial environment that makes it difficult for residents
to participate in social activities, such as fewer participation opportunities and limited infrastructure, may
contribute to a high risk of depression in rural municipalities. Similar results have been obtained by studies conducted among older adults in China, and in particular, anassociation between high depression risk and low social  ≥ 85

1.04

Median rate ratio

Standard errors in parentheses

0.001(0.002)

0.002(0.002)

Betweenneighborhood­variancea

1.03

1.10

1.13

Median rate ratioa0.009 (0.003)

Between munici- 0.016 (0.004)
pality ­variancea

Random-effect part of the model

1.13 [1.09,1.17]

1.38 [1.30,1.48]

  75–84

Age (ref. 65–74)

Individual-levelfactors

  Reciprocity

  Social cohesion

  Civic participation

Community social capital

0.92 [0.87,0.99]

  Long  Longest

1.00

0.000(0.000)

1.07

0.005 (0.002)

1.39 [1.31,1.49]

1.13 [1.09,1.17]

0.87 [0.85,0.90]

0.89 [0.83,0.96]

0.91 [0.83,0.99]

0.90 [0.81,1.00]

  Middle

0.98 [0.90,1.06]
0.97 [0.91,1.04]

0.97 [0.88,1.06]

0.94 [0.86,1.04]

  ShortTime to the DID (ref. shortest)

Neighborhood-level factors

1.08 [0.99,1.18]

1.22 [1.15,1.30]

1.16 [1.06,1.26]

  Low

  Lowest

0.96 [0.88,1.05]

0.94 [0.86,1.04]

1.10 [0.95,1.28]

0.99 [0.89,1.11]

1.09 [0.98,1.21]

Model 2

  Middle

Model 1

  HighPopulation density (ref. highest)

Municipality-level factors

Null

1.00

0.000(0.000)

1.09

0.007 (0.003)

1.40 [1.32,1.49]

1.13 [1.10,1.17]

0.88 [0.86,0.90]

1.00 [0.90,1.10]

0.94 [0.87,1.02]

0.97 [0.90,1.04]

0.99 [0.90,1.09]

1.20 [1.10,1.33]

1.24 [1.14,1.36]

1.04 [0.92,1.18]

1.09 [0.96,1.24]

Model 3

Table 3 Prevalence ratios [95% confidence intervals] of depressive symptoms among men: the results of multilevel analysis

1.00

0.000(0.000)

1.08

0.007 (0.002)

1.39 [1.30,1.48]

1.13 [1.09,1.17]

0.90 [0.88,0.91]

0.96 [0.88,1.06]

0.96 [0.89,1.04]

0.98 [0.91,1.05]

1.00 [0.93,1.08]

1.16 [1.07,1.27]

1.26 [1.18,1.35]

1.07 [0.96,1.19]

1.08 [0.95,1.22]

Model 4

Kanamori et al. Int J Health Geogr(2021) 20:42
Page 8 of 13  ≥ 85

1.01

Median rate ratio

Standard errors in parentheses

0.000(0.000)

0.000(0.002)

Betweenneighborhood­variancea

1.00

1.11

1.16

Median rate ratioa0.012 (0.004)

Between munici- 0.023 (0.006)
pality ­variancea

Random-effect part of the model

1.17 [1.13,1.23]

1.60 [1.50,1.71]

  75–84

Age (ref. 65–74)

Individual-levelfactors

  Reciprocity

  Social cohesion

  Civic participation

Community social capital

0.97 [0.91,1.03]

  Long  Longest

1.00

0.000(0.000)

1.07

0.006 (0.002)

1.60 [1.50,1.70]

1.17 [1.12,1.22]

0.89 [0.87,0.91]

0.97 [0.90,1.05]

0.95 [0.89,1.02]

0.99 [0.92,1.06]

  Middle

0.96 [0.90,1.02]
1.02 [0.96,1.09]

0.95 [0.88,1.03]

0.99 [0.91,1.08]

  ShortTime to the DID (ref. shortest)

Neighborhood-level factors

1.09 [1.00,1.19]

1.22 [1.13,1.31]

1.16 [1.04,1.29]

  Low

  Lowest

1.00 [0.89,1.12]

0.92 [0.82,1.02]

1.09 [0.99,1.21]

0.96 [0.86,1.06]

1.07 [0.99,1.17]

Model 2

  Middle

Model 1

  HighPopulation density (ref. highest)

Municipality-level factors

Null

1.00

0.000(0.000)

1.10

0.010 (0.003)

1.61 [1.51,1.72]

1.18 [1.13,1.23]

0.91 [0.90,0.93]

1.07 [0.98,1.16]

0.99 [0.93,1.05]

1.01 [0.95,1.09]

0.97 [0.90,1.05]

1.20 [1.07,1.34]

1.23 [1.12,1.35]

0.99 [0.89,1.10]

1.08 [0.98,1.20]

Model 3

Table 4 Prevalence ratios [95% confidence intervals] of depressive symptoms among women: the results of multilevel analysis

1.00

0.000(0.000)

1.10

0.010 (0.003)

1.60 [1.50,1.70]

1.17 [1.12,1.22]

0.94 [0.92,0.96]

1.03 [0.96,1.10]

0.99 [0.93,1.05]

1.02 [0.95,1.09]

0.97 [0.90,1.04]

1.16 [1.04,1.29]

1.24 [1.15,1.32]

0.99 [0.90,1.09]

1.08 [0.97,1.19]

Model 4

Kanamori et al. Int J Health Geogr(2021) 20:42
Page 9 of 13

Kanamori et al. Int J Health Geogr(2021) 20:42

participation has been reported [52]. Limited availability of public transportation in rural municipalities could
reduce social participation [53], resulting in poor mental
health [54–56]. Moreover, rural municipalities tend tohave less civic participation overall, regardless of the variability in the characteristics of the neighborhood within
the municipalities. In fact, municipalities with a low
population density include many neighborhoods withlong travel times to reach DIDs (Additional file 5: Fig. S1).
On the other hand, even in neighborhoods from whichreaching DIDs takes a long time, the level of civic participation tended to be higher if the neighborhoods werelocated in municipalities with high population density.
Based on the potential difficulty in social environments
related to municipality-level rurality, in rural municipalities, the frequency and variability of social gathering opportunities may be limited, making it difficult forpeople to find gatherings that interest them. In Japan,
driven by the strong recommendation and financial
incentive schemes of the central government, municipal
governments have provided support for local people toset up “community salons” (voluntary social gathering
activities) for older people. However, compared to urban
municipalities, rural municipalities have fewer salons[57]. Regional differences in local norms for participatingin hobbies and other social activities may also explain thefindings of this study. For example, while conducting thisresearch, several government officials in rural municipalities introduced the following statements by residents: “I
can’t go to the salon because (if I go there) neighbors may
say I am playing without doing any work.” Compared tourban municipalities, in rural municipalities, more people participated in “semi-formal” local associations suchas senior citizens’ associations and community councils,
but fewer people participated in informal activities, asmentioned above [58].
On the other hand, to interpret the results showing alower prevalence of depressive symptoms in rural neighborhoods than in urban neighborhoods and indicatingthat neighborhood-level cohesion and reciprocity are
partly explained the rurality/depression association, it
may be instructive to focus on the characteristics of community social capital stemming from the sociodemographic characteristics of rural neighborhoods. In JAGES,
rural neighborhoods had a high number of people withextensive farming experience and fewer people living
alone (Additional file 4: Table S4). Neighborhood cohesion and reciprocity fostered by communal work throughfarming may have a preventive effect against depression
among farmers. In the farming community, there aremany types of communal work, such as the management of waterways and prevention of damage from wildlife. For example, in areas with low farm density, where

Page 10 of 13

the availability of job-related mutual support is limited,
the prevalence of depression among farmers is high[59]. Nonetheless, the effects of fostered social capitalthrough farming could spill over to non-farmers in thesame neighborhood [60]. In contrast, reverse causation
should also be considered. That is, depressed people may
be less connected to the community and live alone, which
is another known risk factor of depression, and they may
reside in more convenient city centers. However, evenafter adjusting for individual socioeconomic status, thetrend toward less depression in rural neighborhoods
remained (Additional file 1: Table S1).
The policy implications of this study are twofold. First,
the evaluation of the prevalence of depression by area,
in combination with regional characteristics, including
rurality, is essential. If depression is more prevalent inrural areas than in urban areas, developing interventions
that match the characteristics of the area is required, taking into account the possible meanings of the rurality
assessed at each level, as discussed, the rurality evaluatedat large units can represent policy-relevant social structures, and that evaluated at small units tends to reflect
cultural and normative aspects of social relations. For
example, the JAGES initiative, which provided the data
used in this study, has provided participating municipalities with a tool for visualizing differences in health andrelated social factors among and within municipalities[61]. In promoting knowledge translation and evidencebased community-based care [62, 63], the researcher’s
assistance provided to municipal offices has a positive
impact on residents’ health and socioeconomic equity[64]. The findings of this study, which was conductedin Japan, the most aged society in the world, could beapplied to other countries, including Asian nations,where many countries are ageing at a faster rate thanJapan. For example, nationwide population aging is likely
to induce population decline and economic inactivity inrural areas, leading to socioeconomic disadvantages, limited access to healthcare and other public services [65],and poor mental health [9, 10, 12] for residents. Consequently, population aging may cause urban/rural disparities in mental health. A positive aspect is that in small
communities, such as the neighborhood level in thisstudy, the potential preventive effect of community social
cohesion and reciprocity on depression may exist [66].
The effective use of social capital in small communities
may buffer the structural disadvantages in rural areas.
These may be more applicable to countries with similar
cultural/industrial features, including those where rice
farming is common [67].
This study has several strengths and limitations thatshould be highlighted. The main strength of this study isthat it used data from residents and not hospital patients;

Kanamori et al. Int J Health Geogr(2021) 20:42

thus, the results are not biased by the differences between
urban and rural areas with respect to the ease of diagnosis due to healthcare access. In addition, our approach oflarge-scale sampling in the general population residing
in various areas is unlikely to be biased by geographical variations in access to healthcare and allowed us toconsider multiple regional units simultaneously. Regarding limitations, first, the generalizability of our findings
may be limited by the coverage of the sampling frame,
given that our dataset was not representative of the entire
Japanese population. Second, as the data collected inthis study were self-reported, this may have given rise tovarious forms of bias, such as recall bias and social desirability bias. Third, this manuscript focuses on one country (Japan), and the results may not be generalizable toother countries, particularly those with different social
infrastructure, cultures, and mental healthcare provision. Rurality is a diverse and relative concept, and eachcountry’s definition of rurality and the contexts reflectedby those definitions can vary greatly [6]. Fourth, we did
not analyze air pollution levels, proximity to roads, green
areas, or traffic noise because of a lack of access to these
data. Fifth, we were not able to identify and discuss thediversity of rural communities.

Conclusions
The results of this study suggest that the association
between rurality and depression varies according to
the geographical unit analyzed. In rural municipalities,
the risk of depression may be higher for both men andwomen, and the presence of an environment conducive
to civic participation may contribute to a higher risk ofdepression, as observed in this study. The risk of depression in men may be lower in rural neighborhoods inJapan, which may be related to high social cohesion andreciprocity.
Abbreviations
DID: Densely inhabited district; JAGES: Japan Gerontological Evaluation Study.

Page 11 of 13

Additional file 5: Figure S1. Distribution of mean community civic participation evaluated by neighborhood through municipality-level rurality
and neighborhood-level rurality.
Additional file 6: Figure S2. Estimated prevalence of depressive symptoms with 95% confidence intervals by gender: cross-level interaction
between municipality-level rurality and neighborhood-level rurality on
depressive symptoms. The estimates were derived from a three-level
multilevel Poisson regression adjusted for age as well as municipality-level
rurality and neighborhood-level rurality.
Acknowledgements
Editorial support in the form of medical writing, assembling tables, creating
high-resolution images based on the authors’ detailed directions, collating
author comments, copyediting, fact checking, and referencing was provided
by Editage, Cactus Communications, and funded as indicated above. We thankeveryone who participated in the Japan Gerontological Evaluation Study(JAGES).
Authors’ contributions
MK conceived the ideas, designed the study, analyzed the data, and drafted
the manuscript. MH and DT provided the technical support. NK, TO, and KK
played primary roles in the data collection. NK helped to conceptualize anddesign the study. DT and AA helped conceptualize and interpret the data. All
authors read and approved the final manuscript.
FundingMariko Kanamori and Airi Amemiya are research fellows of the Japan Society
for the Promotion of Science (19J21563, 19J1137). MK was also supportedby the Graduate Program for Social ICT Global Creative Leaders (GCL) at the
University of Tokyo. This study used data from the JAGES (Japan Gerontological Evaluation Study), and this work was supported by the MEXT (Ministryof Education, Culture, Sports, Science and Technology, Japan)-Supported
Program for the Strategic Research Foundation at Private Universities (2009–2013), JSPS (Japan Society for the Promotion of Science) KAKENHI Grants(JP18390200, JP22330172, JP22390400, JP23243070, JP23590786, JP23790710,
JP24390469, JP24530698, JP24683018, JP25253052, JP25870573, JP25870881,
JP26285138, JP26882010, JP15H01972, 18H04071, JP18KK0057, JP19H03860,
and 20H00557), Health Labor Sciences Research grants (H22-Choju-Shitei-008,
H24-Junkanki [Seishu]-Ippan-007, H24-Chikyukibo-Ippan-009, H24-ChojuWakate-009, H25-Kenki-Wakate-015, H25-Choju-Ippan-003, H26-IrryoShitei-003 [Fukkou], H26-Choju-Ippan-006, H27-Ninchisyou-Ippan-001,
H28-Choju-Ippan-002, H28-Ninchisyou-Ippan-002, H30-Kenki-Ippan-006,
H30-Junkankitou-Ippan-004, 19FA2001, and 19FA1012), Japan Agency for
Medical Research and Development (AMED; JP17dk0110017, JP18dk0110027,
20dk0110037h9902, and JP18ls0110002), Japan Science and Technology
Agency (JST-OPERA: JPMJOP1831 and JP18le0110009), and the Research
Funding for Longevity Sciences from the National Center for Geriatrics andGerontology (24-17, 24-23, 29-42, 30-22 and 21-20). The views and opinions
expressed in this article are those of the authors and do not necessarily reflect
the official policy or position of the respective funding organizations.

Supplementary Information

Availability of data and materials
The datasets used and/or analyzed during the current study are available fromthe corresponding author on reasonable request.

The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12942-​021-​00296-8.

Declarations

Additional file 1: Table S1. Prevalence ratios [95% confidence intervals]of depressive symptoms: results of the multilevel analysis using individual
sociodemographic characteristics
Additional file 2: Table S2. Prevalence ratios [95% confidence intervals]of depressive symptoms among men: results of the sensitivity analyses
Additional file 3: Table S3. Prevalence ratios [95% confidence intervals]of depressive symptoms among women: results of the sensitivity analyses
Additional file 4: Table S4. Proportion of men and women living alone
and of farmers by neighborhood-level rurality

Ethics approval and consent to participate
The JAGES was approved by the Ethics Committee at the National Center for
Geriatrics and Gerontology (992), Chiba University Faculty of Medicine (2493),
and University of Tokyo Faculty of Medicine (10555). Written informed consent
was assumed upon the voluntary return of the questionnaire. The above-mentioned ethics committees approved the use of this assumed consent upon
the return of the questionnaire.
Consent for publication
Not applicable.

Kanamori et al. Int J Health Geogr(2021) 20:42

Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Health and Social Behavior, Graduate School of Medicine,
The University of Tokyo, Bldg. 3, 7‑3‑1 Hongo, Bunkyo‑ku, Tokyo, Japan.
2
Department of Social Epidemiology, Graduate School of Medicine, Science
Frontier Laboratory, Kyoto University, Floor 2, Yoshida‑konoe‑cho, Sakyo‑ku,
Kyotoshi, Kyoto, Japan. 3 Department of Social Preventive Medical Sciences,
Center for Preventive Medical Sciences, Chiba University, 1‑33, Yayoicho,
Inage‑ku, Chiba, Chiba, Japan. 4 Department of Gerontological Evaluation,
Center for Gerontology and Social Science, National Center for Geriatrics
and Gerontology, 7 Chome 430, Moriokacho, Obu, Aichi, Japan. 5 Departmentof Community Health and Preventive Medicine, Hamamatsu University Schoolof Medicine, 1‑20‑1 Handayama, Higashi‑ku, Hamamatsu, Shizuoka, Japan.
6
Design Research Institute, Chiba University, 1‑19‑1, Bunka, Sumida‑ku, Tokyo,
Japan.
Received: 13 July 2021 Accepted: 7 September 2021

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