Health & Place 74 (2022) 102764

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Health and Placejournal homepage: www.elsevier.com/locate/healthplace

Effectiveness of a community organizing intervention on mortality and its
equity among older residents in Japan: A JAGES quasi-experimental study
Maho Haseda a, 1, Daisuke Takagi b, Andrew Stickley a, 1, Katsunori Kondo c, d, Naoki Kondo a, *, 1, 2aDepartment of Health Education and Health Sociology, School of Public Health, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan
Department of Health and Social Behavior, School of Public Health, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japanc
Center for Preventive Medical Sciences, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 360-0856, JapandCenter for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430, Moriokacho, Obu-shi, Aichi, 474-8511, Japanb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Community organizing
Quasi-experimental study
Aged
Mortality
Socioeconomic status

Interventions that involve key aspects of community organizing, such as quantitative community assessments
and organizational partnership support for the community, may promote residents’ health. We evaluated theeffectiveness of this form of intervention on mortality and its variability across individual-level household
equivalized income tertiles, comparing 52,858 residents aged 65 and above in 12 intervention municipalities to39,006 residents in nine control municipalities in Japan. During 1,166 days of follow-up, the adjusted hazard
ratio for cumulative mortality among men in the intervention municipalities was 0.92 (95% confidence interval:
0.86, 0.99) compared to those in the control group, with similar results being observed across all income levels.
Active utilization of data to evaluate communities and building intersectoral partnerships might lower older maleresidents’ mortality risk, regardless of their income status.

1. Introduction
The growing number of disabled older adults is a major public healthissue in an era of global population aging (World Health Organization,2017). Reoccurring global health crises, including natural disasters andinfectious disease outbreaks, heighten the importance of creating resil­
ient communities that provide the opportunity for primary health andsocial care for older adults (Goldman et al., 2014; Hikichi et al., 2015;
Holt-Lunstad et al., 2010; World Health Organization and the United
Nations Children’s Fund, 2020). To do this, interventions that empower
communities, strengthening organizational partnerships with multiple
public and private sector bodies, as well as with civic organizations, arerequired (Ministry of Health, Labour and Welfare, 2012; Naidoo andWills, 2016; World Health Organization, 2017, 2008). Minkler definedcommunity organizing as “the process by which community groups arehelped to identify common problems or change targets, mobilize re­sources, and develop and implement strategies to reach their collective
goals” (Minkler, 2012). A community organizing approach is useful inmeeting residential intervention needs, creating networks and dialogue

with various stakeholders to provide support to residents (Minkler,2012).
To carry out community organizing, community assessment andintersectoral partnership building are fundamental skills for practi­
tioners in local public health sectors (Ståhl et al., 2006). However, as
there is evidence that local public health staff often have insufficient
skills and resources to use epidemiological data or collaborate withother sectors or organizations, supportive frameworks for staff members
may be required (Larsen et al., 2014; Morikawa et al., 2016; Ollila,2011). Recent studies have suggested that broad cooperation amongindigenous social agents and grassroots organizations is associated withresidents’ better health, improved control of chronic diseases,
self-efficacy, and public health emergency preparedness (DeCoster andGeorge, 2005; Gil-Rivas and Kilmer, 2016; Jung and Viswanath, 2013;
Larsen et al., 2014; Lawn et al., 2008). However, to the best of ourknowledge, as yet, few studies have assessed the effectiveness of com­munity organizing interventions aimed at older adults, and most of them
have been small, involving only a few communities (Shearer et al.,2012).

* Corresponding author. Yoshida-Konoe-cho, Sakyo-ku, Kyoto-shi, Kyoto, Japan.
E-mail address: kondo.naoki.0s@kyoto-u.ac.jp (N. Kondo).
1
Department of Social Epidemiology, Graduate School of Medicine and School of Public Health, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto-shi, Kyoto
606–8501, Japan.
2
Institute for Future Initiatives, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.
https://doi.org/10.1016/j.healthplace.2022.102764
Received 16 September 2021; Received in revised form 31 January 2022; Accepted 3 February 2022
Available online 10 February 2022
1353-8292/© 2022 The Authors.
Published by Elsevier Ltd.
This is an open(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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licenseM. Haseda et al.

Health and Place 74 (2022) 102764

Community organizing has been advocated as a valuable interven­tion to address health inequalities (Artazcoz and Rueda, 2007; Waller­
stein et al., 2017; Wallerstein and Duran, 2006). Although equity is the
key dimension of evaluating any public health measure, mosthealth-promoting programs have failed to assess possible differential
health impacts across socioeconomic groups (Donabedian, 1990;
Whitehead, 2007).
In an earlier study, community organizing interventions were asso­
ciated with increased participation in social activities among men
regardless of their income levels (Haseda et al., 2019). Using a longi­
tudinal quasi-experimental design, the current study builds on and ex­
tends this previous research by examining whether this form ofintervention is also associated with mortality and its differential impact
by income in Japan, the country with the world’s most aged population.

Table 1
Components of the planning cycle of JAGES-HEART.

2. Methods

Steps-Brief description

Overall program

Additional support forintervention municipalities

Assessment-Defining the
problem

JAGES researchers
aggregated individual
responses from every survey
by municipalities or
subpopulations and createdcommunity assessment
sheets. These sheets help
with the identification andmonitoring of health issues
in each municipality. They
provide within andbetween-municipality
comparisons on healthstatus by subpopulations.
JAGES researchers provided
these sheets to members
working in the municipality
health sector. Each year,
researchers hold groupsessions with municipality
staff members where
workshops are run on how
to use data for community
health promotion and tohighlight the importance ofsocial determinants ofhealth. Based on thisinformation, staff members
set their own targets toachieve active aging amongolder residents for the next
three years. The targets areincluded in eachmunicipality’s plan for theprevention of frailty in older
people.

(Community assessment
sheets provided byresearchers are basically thesame in both intervention
and control municipalities.
This phase includes theevaluation of the impact ofthe program.)

Response-Setting theagenda2.1. Data
The Japan Gerontological Evaluation Study (JAGES) is an ongoinglarge-scale, population-based research program in Japan (Kondo andRosenberg, 2018). Baseline data were drawn from the 2013 wave of the
JAGES survey. In October to December of that year self-administeredquestionnaires were mailed to 193,694 community-dwelling in­dividuals aged 65 years and above who were functionally independent
in their daily lives living in 32 municipalities which voluntarily partic­
ipated in the program. In large municipalities, multistage random
sampling methodology based on the official residential registers wasused to select respondents, while in small municipalities all eligible
residents were included. Completed questionnaires were received from137,736 individuals (response rate: 71.1%). From the 32 participating
municipalities, 21 that included 91,864 (men: 42,833, women; 49,031)
respondents, provided public long-term care insurance data for
approximately three years (the exact period varied across themunicipalities).
2.2. The JAGES program of supporting municipalities
A detailed description of JAGES activities has been provided else­
where (Haseda et al., 2019 Kondo, 2019; Kondo and Rosenberg, 2018).
To support participating municipalities, the JAGES research team has
used the JAGES Health Equity Assessment and Response Tool (JAGE­
S-HEART), developed with the World Health Organization (Ojima,
2014; World Health Organization, 2010). The components of
JAGES-HEART consist of assessment, response, policy, and program(Table 1). More specifically, after every survey, the JAGES researchteam created community assessment sheets. These sheets provided
within- and between-municipality comparisons on regional health status
for specific subpopulations, as determined by age group, gender, andincome levels. JAGES researchers provided these sheets to members
working in the municipality health sector. Each year, researchers heldgroup sessions using JAGES-HEART for community health promotion
and to highlight the importance of social determinants of health.
2.2.1. Intervention
Among the 32 municipalities that participated in the 2013 JAGES
survey, JAGES researchers established partnerships in 16 municipalities.
We selected these municipalities as the intervention group while theremaining 16 municipalities served as the control group. The interven­tion group consisted of municipalities where researchers had alreadyestablished good working relationships with staff members or where
they thought these relationships could be developed and thus believedthat these municipalities would be good candidates to work with oncommunity organizing using the JAGES-HEART framework. The inter­
vention started when the baseline (2013) survey was completed, and thesurvey data became available.
2

Policy-Developing
policyDetermined by municipality
staff members.

Program-Implementing
the program

Determined by municipality
staff members.

JAGES researchers coachedstaff members on how toutilize community
assessment sheets more
effectively in the specificmunicipality duringconsultations. Staff members
are also instructed on
decision making, healthpromotion and long-termcare measures, with relevant
epidemiological informationor successful examples ofactivities in othermunicipalities being given.
Stakeholders in themunicipalities involved incommunity organizing
activities such as non-profit
organizations or private
sector companies were also
identified and invited to use
their technology and/or
resources in a socially
responsible way to help
implement a multifaceted
approach to achieve healthy
aging in these communities.
Researchers coached staff
members on how to conduct
meetings with potential
partners. Staff members were
expected to utilize
community assessment data
and resources to makepolicies. In intersectoral
meetings that involved
municipality staff members
from a wide range of sectors
and other local stakeholders,
epidemiologic evidence andcommunity assessment
sheets were utilized when
discussing actions andpolicies to be prioritized inorder to improve older
people’s health and wellbeing.
Examples of collaborative
data-oriented actions aimed
at improving older people’s
health include launchingcommunity places-to-go (e.
g., recreational/socializing
salons) and increasing
regular participation in thesesalons and in other existingsocial activities.

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Health and Place 74 (2022) 102764

might also be important when it comes to forming close partnerships
with JAGES researchers, we also used data from a questionnaire survey
with staff, including the number of years since the municipality’s initial
participation in the JAGES program, the number of municipality staff
members involved in JAGES work by their specialty/job types (publichealth nurses, clerical worker or other) and the longest number of yearsof working for the health sector among all municipality staff members.
We calculated the averages of multiple members’ responses by munic­
ipality and assigned them to each municipality as the representativevalues of the municipality.
Using previous research as a guide, as potential confounders we also
considered study participants’ age (categorized in 5-year age groups),
household equivalized income (tertiles categorized as low, middle, andhigh), years of educational attainment (9 or less/10 or more), living
alone or not, marital status (married or other), depressive symptoms,
any health conditions (currently receiving treatment for/experiencing
the after-effects of any disease or not), and instrumental activities ofdaily living (IADL) (Holt-Lunstad et al., 2010; Kagamimori et al., 2009;
Wulsin et al., 1999). Household equivalized income was calculated by
dividing each response to a question asking about annual income by thesquare root of the number of household members. We further divided
the calculated household equivalized income into tertiles. Depressive
symptoms were evaluated using the 15-item version of the GeriatricDepression Scale We set a score of 5 or above as signifying the presenceof at least mild depressive symptoms (Niino et al., 1991; Nyunt et al.,2009; Yesavage and Sheikh, 1986). To assess IADL we used a validated
scale: the Tokyo Metropolitan Institute of Gerontology Index ofCompetence (TMIG-IC) that has a 5-item IADL component. If a respon­
dent answered “no” to two or more items, he/she was categorized ashaving low IADL (Koyano et al., 1991).

These components are based on Urban-HEART developed by World Health Or­
ganization (World Health Organization & WHO Centre for Health Development↱Kobe, Japan↱, 2010).

In the intervention municipalities, researchers provided advice on
how to utilize community assessment data effectively and how tofacilitate intersectoral meetings. In addition, they also supported mu­
nicipality staff members through one-to-one consultations several times
per year. This continuing support included coaching on agenda setting,developing policy, and implementing the steps in the JAGES-HEART
program (Table 1), while these things were determined only by staff
members in the control municipalities. Specifically, researchers pro­vided advice on how to utilize community assessment data more effec­
tively and how to facilitate intersectoral meetings In the community careand intersectoral meetings in the intervention municipalities, epidemi­
ologic evidence and the community assessment sheets that were basedon the survey data were utilized when discussing issues to be prioritizedand effective intersectoral policies to improve the health and well-beingof local older people.
Most of the implemented programs were designed to expedite mu­
nicipalities’ community-based long-term care prevention plans, as rec­ommended by the central government: launching community places-togo and increasing regular participation in social activities. Thus, we
could assume that municipal staff members would come into contact
with all functionally independent older people in their municipalities
through various activities. The specific intervention approaches
depended on the individual context and characteristics within the mu­
nicipality, and the health-related issues or vulnerable subpopulations tobe prioritized (see Table 1, Supplementary Table 1).
2.3. Measurements

2.4. Statistical analysis

2.3.1. Primary outcome
In the current study our primary outcome was the occurrence ofdeath, which was identified with public long-term care insurance pay­
ment data as these data include the date of death. A secondary outcome
was the onset of functional impairment, which was categorized as
qualifying to use public long-term care insurance services. Qualifying
was based on a nationally standardized process, with seven ranks for thelevel of care service needs. Details of the Japanese long-term care in­surance (LTCI) system have been provided elsewhere (Ministry of
Health, Labour and Welfare, 2002; Tamiya et al., 2011). We determinedthat reaching level 2 or above was a marker for the onset of functional
decline. Level 2 relates to individuals who need long-term care for ac­
tivities of daily living.

We used survival analysis to model the length of days until the onsetof an outcome between the intervention and control groups. Fine andGray’s subdistribution hazard model was used to examine the competingrisks between mortality and the onset of functional decline (Fine andGray, 1999). We considered the occurrence of death, onset of functional
decline and residential mobility (moving out to another municipality) as
competing risks for one another. We calculated robust standard errors tocontrol for data clustering within the elementary school district, thesmallest identified geographical unit available in the dataset. We
confirmed the proportionality in survival between the two groups byincluding log-transformed time interactions on all covariates. The
analysis was stratified by gender and adjusted for the explanatory var­iables described above. Since recent studies have shown that community
factors may have a different impact according to gender, we modeled
men and women separately (Eriksson et al., 2011; Haseda et al., 2019;
Pattyn et al., 2011). We also examined the interaction between income
level and the intervention and performed stratified analyses by income
level. This is because community interventions may have different ef­
fects in terms of socioeconomic status, as represented by income, andmonitoring health status by socioeconomic status is necessary to assess
the effectiveness or fairness of interventions (Frohlich and Potvin, 2008;
World Health Organization, 2008, World Health Organization & WHO
Centre for Health Development ↱Kobe, Japan↱, 2010). Missing infor­
mation was modeled by creating a missing category for each variable.
We balanced the sample characteristics of the two groups using
stabilized inverse probability of treatment weighting (IPTW) techniques,calculating the propensity scores of participating municipalities thatwere selected to be in the intervention group. We used stabilized weights
to weight the samples by the inverse of the propensity scores multipliedby the proportion of the sample observed as intervention and control,
respectively (Xu et al., 2010). We calculated the propensity scores byapplying a logit model using a baseline (2013) municipality character­istics variable that might have influenced the selection, as described

2.3.2. Other explanatory variables
As the municipalities were not randomly assigned to the intervention
and control groups, our analysis may have been potentially biased. To
minimize this risk, we also used information on the characteristics ofparticipating municipalities. We gathered data on the municipalities’
characteristics from data sources published by the central government,
including the proportion of the population aged 65 and above, popula­tion density, the proportion of those who qualified for long-term care,
the incidence of new long-term care need cases in the previous year, thestandardized mortality ratio (SMR) of those aged 65 years and above, afinancial capability index, and the number of “community salon” ac­tivities per 10 thousand residents aged 65 and above (Ministry of Health,Labour and Welfare, 2014, Ministry of Health, Labour and Welfare,2013; Statistics Bureau and Ministry of Internal Affairs and Communi­
cations, 2013). The financial capability index shows the fiscal resources
available to the local government. It is an average of the value obtainedby dividing base fiscal revenue by base fiscal demand over the previous
three years. The higher the value, the more financial resources areavailable (Ministry of Internal Affairs and Communications, 2014).
Assuming that the characteristics of the municipality staff members
3

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Health and Place 74 (2022) 102764

above.
We performed our primary analysis on an intention-to-treat basis.
However, we also performed a sensitivity per-protocol analysis using thefrequency of face-to-face meetings between JAGES researchers andmunicipality staff members as a proxy for the strength of the interven­tion instead of the original intervention and control group’s allocation.
We assumed that the more frequently those meetings were held, themore collaborative the relationships would be that were established andthat this would result in more community organizing actions being
implemented (Cross et al., 2002). We divided the municipalities by thefrequency of meetings into three categories: more than twice per year
(representing a continuous intervention using JAGES-HEART), once or
twice per year (likely to have provided a one-sided lecture on thecommunity assessment data by JAGES researchers), and less than once a
year (no consultation or tailor-made feedback). We consulted with re­searchers to decide the cut-off points for the content validity. For thesensitivity analyses, we simply adjusted for theoretically importantmunicipality-level covariates instead of employing the propensity scorebecause of the difficulty in calculating the scores due to strong corre­
lations between the variables. The adjusted variables included theFig. 1. Flowchart of participants.
4

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Health and Place 74 (2022) 102764

proportion of those aged 65 or above, population density, the incidenceof receiving LTCI benefits, the SMR of those aged 65 or above, and thenumber of places-to-go operated by residents.
All analyses were performed with Stata MP, version 15.1 (Stata corp.
Texas, USA). The results are presented as hazard ratios (HR) with 95%
confidence intervals (CI).

adjusted mortality HR for older people in the intervention group was
0.92 (95% CI: 0.86, 0.99), which was lower than in the control group(the P-value for the between group difference = 0.026) (Fig. 2, Sup­
plementary Table 2). Among women, no association was observed be­
tween the intervention and mortality. The mortality HR of women in theintervention group was 1.02 (95% CI: 0.92, 1.12) compared to those in
the control group.
In an analysis stratified by income level, among men, the mortality
risk was marginally lower in the intervention group for both the lowand high-income groups (Fig. 3). Interactions between the intervention
and income levels were not observed for either men (p = 0.998) or
women (p = 0.596).
The risk for the onset of functional disability was not different be­
tween the two groups for either men or women: the adjusted HR for
individuals in the intervention group was 1.06 (95% CI: 0.96, 1.17) for
men and 0.99 (95% CI: 0.87, 1.12) for women (Fig. 4, Supplementary
Table 3).
The per-protocol sensitivity analyses showed similar results, though
they were not statistically significant. Men and women who lived inmunicipalities where JAGES-municipality staff meetings were held morethan twice per year both had HRs below 1compared to the residents inmunicipalities where meetings were held less than once per year. Spe­cifically, the HR was 0.96 (95% CI: 0.87, 1.05) for men and 0.98 (95%
CI: 0.86, 1.12) for women (Supplementary Table 4).

3. Results
Among the 32 participating municipalities, 12 intervention and 9
control municipalities provided follow-up data. We thus analyzed datafrom 91,864 individuals (24,718 men and 28,140 women in the inter­
vention group, 18,115 men and 20,891 women in the control group)(Fig. 1, Table 2). The mean follow-up period was 1,166 days with amaximum period of 1,287 days. During the 277,749 person-years offollow-up, 3,029 men (1,646 in the intervention group and 1,383 in thecontrol group) and 1,562 women (836 in the intervention group and 726
in the control group) died. Meanwhile, 2,246 men (1,300 in the inter­
vention group and 946 in the control group) and 2,122 women (1,159 in
the intervention group and 963 in the control group) developed func­
tional impairment. After the IPTW process, all municipality character­istics were well-balanced between the intervention and control groups:
the standardized differences of all variables were less than 0.1 (Table 3)
and the c statistic was 0.762.
The results of the survival analysis showed that among men, theTable 2
Baseline characteristics and observed outcomes of the community-dwelling older adults.
Men

Women

Intervention (n = 24,718)nDeathsMoved out*
Person-years
Functional disabilities
Person-years
Age
65–69
70–74
75–79
80–84
85Equivalent household income tertiles
Low-income (<1.58 million yen)
Middle-income (1.58–2.46 million yen)
High-income (>2.46 million yen)
Missing
Education<10 years
Missing
FamilyLiving alone
Missing
Marital status
No spouse
Missing
Health status
Any health conditions**
Missing
Physical function
Declining IADL
Missing
Mental health status
Having depressive symptoms
Missing

(%)

1,646
272
77,6351,300
73,340Control(n = 18,115)
n

(%)

1,383
109
57,955946
54,977

Intervention (n = 28,140)
n

(%)

836
351
89,8471,159
85,018Control(n = 20,891)
n

(%)

726
153
68,097963
64,414

6,802
7,481
5,633
3,348
1,454

(27.5)(30.3)(22.8)(13.5)
(5.9)

5,415
5,473
3,803
2,309
1,115

(29.9)(30.2)(21.0)(12.8)
(6.2)

7,455
8,704
6,431
3,761
1,789

(26.5)(30.9)(22.9)(13.4)
(6.4)

5,697
6,177
4,639
2,828
1,550

(27.3)(29.6)(22.2)(13.5)
(7.4)

5,769
6,227
9,503
3,219

(23.3)(25.2)(38.5)(13.0)

4,434
4,395
6,362
2,924

(24.5)(24.3)(35.1)(16.1)

7,253
5,961
8,594
6,332

(25.8)(21.2)(30.5)(22.5)

5,497
3,865
5,904
5,625

(26.3)(18.5)(28.3)(26.9)

8,355
457(33.8)
(1.9)

8,102
327

(44.7)(1.8)

11,058717(39.3)(2.6)

11,172545(53.5)
(2.6)

2,403
1,031

(9.7)
(4.2)

1,183
810

(6.5)
(4.5)

5,315
1,485

(18.9)
(5.3)

2,882
1,274

(13.8)
(6.1)

3,571
459

(14.5)
(1.9)

2,200
421

(12.1)(2.3)

10,873816(38.6)
(2.9)

7,660
814

(36.7)(3.9)

17,9383,038(72.6)(12.3)

13,102
2,288(72.3)(12.6)

20,2603,650(72.0)(13.0)

15,2732,762(73.1)(13.2)

1,872
629

(7.6)
(2.5)

1,845
565

(10.2)
(3.1)

1,254
659

(4.5)
(2.3)

1,400
765

(6.7)
(3.7)

6,521
2,328

(26.4)
(9.4)

4,878
1,847

(26.9)(10.2)

7,092
4,367

(25.2)(15.5)

5,326
3,363

(25.5)(16.1)

Abbreviations: IADL, Instrumental Activities of Daily Living.
* Moved out: lost to follow-up after moving to another municipality.
**Any health conditions: currently receiving treatment for, or experiencing the after-effects of cancer, stroke, heart disease, hypertension, diabetes mellitus, dysli­
pidemia, respiratory disease, gastrointestinal disease or liver disease, kidney or prostate gland disease, musculoskeletal disease, traumatic injury, blood or immune
system disease, psychiatric disease, dementia, Parkinson’s disease, visual impairment, hearing impairment, and others.
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Health and Place 74 (2022) 102764

Using more data and a more rigorous analytic approach (IPTW), we haveadded new evidence by showing that community organizing in­terventions might even reduce mortality.
Several potential mechanisms may explain our findings. The inter­
vention might have developed the capacity of local staff members toplan and practice health-promoting activities targeting social de­terminants of health. Such interventions may foster individual-levelsocial capital in local staff members which can lead to collaborativesynergy and the better governance of municipalities. In turn, this may
facilitate the planning and implementation of health-promoting strate­
gies matched with each community context, and in this way, enable
older adults to enhance their mental health and functional abilities forbasic, instrumental, and social activities of daily living, thus alleviating
the risk of premature mortality (Haseda, 2018; Haseda et al., 2019;
Takahashi et al., 2019; Watanabe et al., 2019; World Health Organiza­
tion, 2015). This is in line with the process described in the community
coalition action theory, a practical framework for community organizing(Butterfoss, 2013). Another possibility is that the partnership with ac­ademic human resources in a community health promoting program had
anadditive
effect.
Empirical
evidencesuggeststhatcommunity-academic partnerships that incorporate the same compo­
nents as the JAGES program may result in beneficial outcomes such astrusted and respectful interpersonal relationships, that can lead todata-oriented actions, etc (Cargo and Mercer, 2008; Drahota et al., 2016;
Kondo and Rosenberg, 2018).
We can only speculate why the intervention was beneficial for men
but not women. First, men might have more room to improve in terms of
their functional ability. A previous study found that a 3-year interven­tion increased the participation of men in the community more than
women (while women did not show a large improvement perhaps due toa ceiling effect) (Haseda et al., 2019). Second, our intervention suc­ceeded in identifying and targeting a priority subpopulation (oldermen). Many local practitioners seem to understand the less socially
active status of older men than women in the community (based on our
personal communications). Therefore, data showing that status in aquantitative manner may be a powerful motivating factor that pushes
practitioners to target this specific subpopulation. Indeed, in some mu­
nicipalities, staff members have already started recruiting volunteers bytargeting retired men with special calls to participate as steering com­mittee members in neighborhood residential associations and otherinformal activity groups (Hikichi et al., 2015; Kondo and Rosenberg,
2018).

Table 3Baseline characteristics of the 21 municipalities.
Intervention(n = 12)Control (n =9)mean [SD]
Demographic
Proportion of adults
24.5
[5.2]
aged ≥65, %Proportion of older
16.9
[2.6]
people using LTCI,%
Incidence of
4.6
[0.6]
certified LTCI, %Standardized
0.97[0.06]
Mortality Ratio(aged ≥65)
Financial
1.8[1.8]
Capability IndexaCity Index
0.7
[0.3]
Number of
1.7[0.8]
community salons(/10,000 aged ≥65)
Years since
5.3
[3.6]
participating in
JAGES
Characteristics of municipality staff
Proportion of office
0.3
[0.3]
workers
Longest years in
7.9
[7.2]
serviceStandardized
Difference
Raw

Weighted

23.1

[2.5]

− 0.24− 0.05

15.3

[1.5]

− 0.05− 0.025.4[4.4]− 0.06− 0.031.07[0.08]− 0.607− 0.086

4.4

[5.8]

0.254

0.076

0.8
1.8

[0.2]
[0.4]

0.3
0.09

0.03
− 0.096.8[4.1]− 0.22− 0.020.2[0.3]− 0.03

0.02

6.5

[5.1]

0.1

− 0.03Abbreviations: SD, standard deviation; LTCI, Long-Term Care Insurance.
aCity Index = Categories of residential population density (1: <1000/km2,2:
1000–4000/km2, 3: >4000/km2).

4. Discussion
The results of survival analyses showed that the mortality risk was
lower among older men living in municipalities where JAGES re­searchers actively supported municipality staff members for community
organizing actions. This result was similar across all income levels.
However, the intervention was not associated with the risk for the onset
of functional disability.
Our findings are in line with an earlier observational study in Swedenthat evaluated a “safe-community” program through intersectoral ac­
tions that had taken place in a municipality, which reported reduced
falls and injuries in adults aged 65–79 years (Lindqvist et al., 2001).

Fig. 2. Cumulative mortality: results of proportional hazard model analyses.
6M. Haseda et al.

Health and Place 74 (2022) 102764

Fig. 3. Cumulative mortality by gender and income levels.

Fig. 4. Cumulative incidence of functional disability by gender: results of proportional hazard model analyses.

The possibly beneficial effects of the intervention were observed
regardless of individual income levels, i.e., we did not find evidence ofany modification by income of the effect of the intervention. This con­curs with the findings of a meta-analysis showing that community
engagement interventions have a positive impact on the health andpsychosocial aspects of disadvantaged populations (O’Mara-Eves et al.,2015). A possible reason for why there was a positive impact irre­
spective of income levels might be linked to the fact that these results arefrom a strategic data-oriented intervention. More specifically, somemunicipalities may have used health data by income level for inter­
vention targeting (Kondo, 2015). Lower income groups might have been
embraced and prioritized for action (e.g., increasing some activities in
the community that are easy for low-income people to participate in)because of the poorer health data among disadvantaged individuals,
Despite the association with mortality, the intervention was not linked
to functional decline. This was an unexpected finding given that a
number of studies have shown that differences in functional decline arealso associated with social participation (Douglas et al., 2017; Takahashi
et al., 2019). This might have been due to measurement error linked tothe outcome. We defined the onset of functional decline as becoming
qualified for public long-term care insurance benefits. In intervention
communities, it is possible, however, that residents may have hadgreater access to long-term care services as a result of the building of astronger network of stakeholders. This situation may have enhanced thepotential demand for formal support, resulting in more “disability”
onset. Our intervention might have also been insufficient to prevent the7

M. Haseda et al.

Health and Place 74 (2022) 102764

transition to a functional disability status for individuals who wereseverely frail at the baseline.
This study has several limitations. First, some bias might have been
induced when allocating the municipalities into the intervention andcontrol groups. In some municipalities, close JAGES-municipality
collaborative actions had already been established before the baseline.
In this case, the HRs may reflect effects carried over before baseline.
Nonetheless, we believe that our balancing of the propensities of being
selected for the intervention, that even considered the personal char­acteristics of municipality staff, minimized the impact of potential
confounders. This being said, as it was not possible to allocate munici­
palities randomly in this study, future studies which randomly allocate
municipalities participating in the program at the same time point are
required to ensure there is no potential confounding. Second, residual
confounding may have existed. For example, the participating munici­
palities might have been receiving external support from non-JAGES
bodies (e.g., other universities and commercial consultation services).
It is also possible that the pre-intervention mortality trends might havebeen different between the two groups. However, we were not able toexamine this issue due to a lack of suitable data. Additionally, wegrouped missing data into a missing category in our analyses which may
have introduced bias. Further studies are needed where other ap­
proaches are adopted for dealing with missing data, such as throughmultiple imputation. Third, municipality staff members in all munici­
palities participating in the JAGES program might have been highly
motivated to engage in community organizing actions from the begin­ning of this study, even if they were subsequently allocated to the con­trol group. This might have resulted in an underestimation in the effectsize we observed. Fourth, the follow-up period might not have been
sufficient to determine whether the intervention had been truly effec­
tive. In addition, given that there was only a marginally beneficial effectof the intervention on mortality among men, the results might have been
observed by chance. Further studies examining the long-term effects andunderlying mechanisms behind these changes using a longer follow-up
period and more precise health-status data are necessary. Fifth, theintervention might not have been consistent across the intervention
group and thus may have violated the consistency assumption since thespecific content of the efforts in each municipality differed according to
the context. However, the main framework and concept used wereconsistent among all intervention municipalities. Furthermore, thisintervention framework allowed flexible support according to the situ­
ation of the local government, and such flexibility may have had abeneficial effect in terms of the intervention.

Ethical approvals
This study was approved by the ethics board of the Faculty of Med­icine, the University of Tokyo (approval No:10,555). The 2013 JAGES
wave was approved by the ethics committee of the Nihon Fukushi
University (approval No: 13–14).
Declaration of competing interest
None declared.
Acknowledgements
This study used data from the Japan Gerontological Evaluation Study(JAGES), which was supported by JSPS (Japan Society for the Promotion of
Science) KAKENHI Grant Number (JP15H01972, 19K21468, 20H00557,
21K17308), Health Labour Sciences Research Grant (H28-Choju-Ippan002, 19FA1012, 19FA 2001, H30− Junkankitou− Ippan− 004), Japan
Agency for Medical Research and Development (AMED) (JP18dk0110027,
JP18ls0110002, JP18le0110009, JP20dk0110034, JP21dk0110037,
JP21lk0310073), Open Innovation Platform with Enterprises, ResearchInstitute and Academia (OPERA, JPMJOP 1831) from Japan Science andTechnology (JST), a grant from the Innovative Research Program on Sui­cide Countermeasures (1–4), a grant from Sasakawa Sports Foundation, a
grant from the Japan Health Promotion & Fitness Foundation, a grant fromthe Chiba Foundation for Health Promotion & Disease Prevention, the 8020
Research Grant for fiscal year 2019 from the 8020 Promotion Foundation(adopted number: 19-2-06), a grant from Niimi University (1915010), a
grant from Sompo Welfare Foundation, grants from Meiji Yasuda LifeFoundation of Health and Welfare, and Research Funding for Longevity
Sciences from the National Center for Geriatrics and Gerontology (29–42,
20-19, 21-20). We thank local municipality staff members and the re­searchers participating in the JAGES group. The sponsors had no role in the
study design, in the collection or interpretation of the data, in writing thereport, or in the decision to submit the article for publication.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.healthplace.2022.102764.
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Supporting municipality staff members in two key aspects of com­munity organizing – utilizing quantitative community data and building
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