International Journal of Public Health
ORIGINAL ARTICLE
published: 14 September 2022
doi: 10.3389/ijph.2022.1604901

Long-Term Trend in the Association
Between Disaster Damage andHappiness Before and After the Great
East Japan Earthquake
Masato Nagai 1*, Hiroyuki Hikichi 2, Koichiro Shiba 3,4, Katsunori Kondo 5,6, Ichiro Kawachi 3
and Jun Aida 1
1

Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University,
Tokyo, Japan, 2Division of Public Health, Kitasato University School of Medicine, Sagamihara, Japan, 3Department of Social and
Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States, 4Department of Epidemiology,
Harvard T.H. Chan School of Public Health, Boston, MA, United States, 5Department of Social Preventive Medical Sciences,
Center for Preventive Medical Sciences, Chiba University, Chiba, Japan, 6Department of Gerontological Evaluation, Center for
Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan

Objectives: Disasters change survivors’ living circumstances, which can affect theirhappiness. We examined the trends in the association between disaster damage andhappiness before and after a disaster.
Methods: We analyzed 4,044 participants aged ≥65 years who had experienced theGreat East Japan Earthquake in 2011. The baseline survey was conducted 7 months
before the disaster. Follow-up surveys have been conducted every 3 years. Using amixed model for repeated measures, we compared the prevalence ratios (PRs) for
unhappiness according to the survivors’ level of housing damage, which is a proxy fordisaster damage.
Edited by:
Nino Kuenzli,
Swiss Tropical and Public HealthInstitute (Swiss TPH), Switzerland
Reviewed by:
Hyo Lee,Dongseo University, South Korea*Correspondence:
Masato Nagai
masato.nagai@ompu.ac.jp
Received: 07 March 2022
Accepted: 30 August 2022
Published: 14 September 2022
Citation:
Nagai M, Hikichi H, Shiba K, Kondo K,
Kawachi I and Aida J (2022) LongTerm Trend in the Association
Between Disaster Damage andHappiness Before and After the Great
East Japan Earthquake.
Int J Public Health 67:1604901.
doi: 10.3389/ijph.2022.1604901

Results: The unhappiness in participants who suffered severe damage appeared to behigher than in those with no damage in 2010 (multivariate-adjusted PR: 1.18, 95%
conﬁdence interval: 0.93–1.48). A higher PR was observed after the earthquake in2013 (1.34, 0.79–2.28), while there was no difference in 2016 (1.02, 0.53–1.97) and2019 (1.03, 0.50–2.12).
Conclusion: The prevalence of unhappiness in survivors with severe housing damage
was higher before the disaster. However, the unhappiness gap between people with andwithout housing damage converged during the follow-up.
Keywords: disaster, trend, happiness, housing damage, older people

INTRODUCTION
Happiness is a component of well-being and refers to positive emotions that reﬂect an individual’s life
satisfaction [1, 2]. While happiness itself is a highly valued outcome [3], it is also associated with healthoutcomes such as mental health [2, 4–6] and suicide [7–9] as well as health behaviors such as drinking [10,11], healthy dietary habits [11–15], smoking [10, 11, 15], and physical activity [10–22]. Some of theseassociations are likely to be due to reverse causation, that is, poor sleep causing unhappiness.

Int J Public Health | Owned by SSPH+ | Published by Frontiers

1

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

On 11 March 2011, the Great East Japan Earthquake, which
registered a magnitude of 9.0 on the Richter scale, struck thenortheast Paciﬁc coast of northern Japan and caused a tsunami
and nuclear plant meltdown. This devastating disaster affected
the lives of 18,000 people and caused widespread property
destruction [23]. Beyond the immediate loss of lives anddamage, the major disaster also caused changes in thesurvivors’ living circumstances, which could potentiallyinﬂuence happiness in the long-term period. For example,
numerous victims were forced to relocate following home loss
or radiation leakage.
This study aims to examine the long-term trajectory ofhappiness among disaster survivors from 2010 to 2019, as
little is known regarding this subject. Hypothetically, it ispossible that survivors may ﬁnd their reference points shifting
as a result of suffering major damage (e.g., property loss), thereby
forcing them to rethink their life goals and values. That is, in theimmediate aftermath of a disaster, people may experience
increased unhappiness, but their goals and expectations may
get adjusted over time, leading them to ultimately return to alevel of happiness comparable to pre-disaster levels. On the other
hand, some survivors may experience prolonged unhappinessdue to ongoing difﬁculties, such as health problems and ﬁnancial
insecurity.
Additionally, previous studies on the association between
disaster damage and health outcomes among disaster victims
have generally collected data only after the disaster. There is apossibility that there is a pre-existing difference in healthoutcomes between damage situations before the disaster.
Hence, it is important to understand how much of theunhappiness can be attributed to traumatic disaster
experiences pre-dating the event. A unique strength of ourstudy design is that the measurement of happiness was
conducted 7 months prior to the occurrence of the earthquake
and tsunami.

FIGURE 1 | Flow diagram of the study participants (Iwanuma, Japan,2010).

who died or moved away from Iwanuma during the follow-up
period.
For the current analysis, we excluded 109 participants whodied before or on the day of the earthquake. In addition,
741 participants with missing information about happiness,
37 participants who responded “Other” to educational status,and 26 participants who responded “Other” to marital status in
the 2010 survey were excluded. Consequently, 4,044 participants
aged ≥65 years were included in the analysis (Figure 1).
The survey protocol was reviewed and approved by the Ethics
Committees on Research of Human Subjects at the Harvard T.H.
Chan School of Public Health (CR-23143), Tohoku University(21-40, 24-29), Nihon Fukushi University (10-05, 13-14), andChiba University (2493). Informed consent was obtained at thetime of data collection at Nihon Fukushi University (ApprovalNo. 10-05). Written informed consent was obtained upon thevoluntary return of the questionnaire.

METHODS
Study Participants

Disaster Damage

A longitudinal study was conducted using repeatedly measured
data from the Iwanuma Study, which is part of a larger
nationwide cohort study conducted, called the JAGES (Japan
Gerontological Evaluation Study) [24], which was established in2010. The JAGES is a large-scale prospective cohort study that isdesigned to describe the health status and social determinants ofhealth in people aged 65 years or over without long-term careneeds. Iwanuma is located approximately 80 km west of theepicenter of the 2011 earthquake. At baseline, a census wasundertaken of all residents of Iwanuma aged 65 years or older.
Surveys were mailed in August 2010, 7 months before theearthquake. Of the 8,576 eligible residents of Iwanuma,
5,058 responded (response rate: 59.0%). After the earthquake
and tsunami, we followed up with 4,957 respondents, excluding
those with invalid IDs or lacking sex/age information (n = 101).
Follow-up surveys have been conducted with these participants
every 3 years via a door-to-door survey that excluded participants

Int J Public Health | Owned by SSPH+ | Published by Frontiers

In this study, we considered housing damage as a proxy forcomprehensive disaster damage. Damage from the Great East
Japan Earthquake was mainly caused by the tsunami, andresidential houses were damaged by it as well. Housing
damage affects an individual’s life in the long-term, such as
through changes in the living environment and new mortgages.
In 2013, participants were asked a question about the level of
housing damage based on an objective assessment by two or more
technical ofﬁcers inspecting each residential property. The level of
housing damage was based on the ofﬁcial designations
determined by the local government for compensation. Basedon a previous study [25], we categorized the participants into thefollowing three categories: “no damage,” “half/partly destroyed”(response: “Half destroyed” or “Partially destroyed”), or “totally/
mostly destroyed” (response: “Total collapse” or “Partial but
extensive collapse”).

2

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

Happiness

speciﬁcation, in which we combined estimates from
20 imputed datasets [32]. All p values were two-tailed, anddifferences of <0.05 were considered as statistically signiﬁcant.
We used the SAS version 9.4 statistical software package.

Happiness in 2010, 2013, 2016, and 2019 was assessed with thefollowing question, which is part of the Geriatric Depression Scale(GDS) [26, 27]: “Do you feel happy most of the time?”. The
participants were asked to choose “yes” or “no”; those who
responded “no” were deﬁned as “unhappy”.

RESULTS

Confounders

Baseline Characteristics by Level of
Housing Damage

We used the following variables from the 2010 baseline survey as
confounders: sex (men or women), age (65–69 years, 70–74 years,
75–79 years, 80–84 years, or ≥85 years), living alone (yes or no),history of cardiovascular disease (CVD) or cancer (yes or no),smoking (never smoked, quit, or current smoker), drinking (nondrinker, quit, or current drinker), marital status (yes or no), anddepression (yes or no). Depression was evaluated using the GDS[26, 27], and the results were classiﬁed as “no depression”(<5 points), or “depressive symptoms” (≥5 points) [28, 29].
We also controlled for social cohesion, social participation,and reciprocity [30]. Social cohesion was assessed using threequestions: 1) “Do you think that people living in your community
can be trusted in general?” (Generalized trust); 2) “Do you thinkpeople living in your community try to help others in mostsituations?” (Mutual help); and 3) “How attached are you to thecommunity in which you live?” (Community attachment).
Responses were measured on a ﬁve-point Likert scale (1 Very,
2 Moderately, 3 Neutral, 4 Not really, or 5 Not at all). Similarly,social participation was evaluated using three questions: 1) “Howoften do you attend sports club activities?”; 2) “How often do you
attend hobby groups?”; and 3) “How often do you attend
volunteer groups?”. Responses were measured as frequencies(1 ≥ 4 times/week, 2 2–3 times/week, 3 1 time/week,
4 1–3 times/month, 5 A few times/year, or 6 Never).
Reciprocity was evaluated using three questions: 1) “Do you
listen to someone’s concerns and complaints?” (Providing
emotional support); 2) “Do you have someone who listens toyour concerns and complaints?” (Receiving emotional support);and 3) “Do you have someone who looks after you when you aresick for a few days?” (Receiving instrumental support). The
possible responses were “yes” or “no”.
In model 2, we additionally adjusted for socioeconomic status(SES), that is, education level (<10 years, 10–12 years,or ≥13 years), current working (yes or no), and equivalent
income (quartile).

Table 1 shows the characteristics of the study participants
according to the categories of housing damage for men andwomen. More severe housing damage was associated with ahigher prevalence of unhappiness in 2010, 7 months before theearthquake (e.g., prevalence = 11.9% for no damage versus 17.5%
for totally/mostly destroyed). The baseline difference inunhappiness decreased during the follow-up period.
In participants with totally/mostly destroyed homes, theprevalence of shorter education years, current working, lower
equivalent income, history of CVD or cancer, current smoker,
depression, poor social cohesion, poor social participation, andpoor reciprocity was the highest compared with the otherparticipants. Meanwhile, the prevalence of current drinker andmarried was the lowest.

The Trends in the Association Between
Housing Damage and Unhappiness
Table 2 shows the association between housing damage and theprevalence of unhappiness in 2010, 2013, 2016, and 2019. After
adjusting for pre-disaster characteristics, the point estimates ofmultivariate-adjusted PR1 in the totally/mostly destroyed homes(versus no damage) increased in 2010 (PR: 1.25, 95% CIs:
0.99–1.57). A similar ﬁnding was observed in 2013 (1.42,
0.84–2.42). Meanwhile, in 2016 and 2019, the prevalence of
unhappiness decreased in participants with totally/mostly
destroyed homes and no obvious difference was observed
between them and those with no damage in 2016 (1.07,
0.56–2.08) and 2019 (1.08, 0.53–2.23). After further
adjustment for SES, such as education level, current working,and equivalent income, the results of multivariate-adjusted
PR2 showed a similar tendency to multivariate-adjusted PR1,
but point estimates were slightly attenuated.
Figure 2 illustrates the prevalence of unhappiness for eachhousing damage category and year predicted from themultivariate-adjusted mixed model for repeated measures. The
prevalence of unhappiness tended to decrease over time in all
categories of housing damage, but this trend was more obvious in
the totally/mostly destroyed category.

Statistical Analysis
A mixed model for repeated measures by Poisson distribution
with robust error variance was used to derive prevalence ratios(PRs) and 95% conﬁdence intervals (CIs) for unhappiness [31].
“No damage” was set as the reference. We included survey years
and its product term by level of housing damage in the model.
The correlation between repeated outcome measurements for thesame participants across different survey years was accounted forby including random effects for the individuals. In themultivariate-adjusted model, we controlled for all pre-disastercharacteristics as potential confounders. Missing information
regarding the level of housing damage and covariates was
imputed via multiple imputation by fully conditional

Int J Public Health | Owned by SSPH+ | Published by Frontiers

DISCUSSION
This study examined trends in the association between disasterdamage and unhappiness after the earthquake from the viewpointof the level of housing damage. We found that the prevalence of
unhappiness might be higher in participants with the most severe

3

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

TABLE 1 | Characteristics by the level of housing damage among 4,044 men and women aged ≥65 years (Iwanuma, Japan, 2010).

No. of participants
Unhappiness in 2010 (%)
Unhappiness in 2013 (%)
Missing
Unhappiness in 2016 (%)
Missing
Unhappiness in 2019 (%)
Missing
Men (%)
Age (%)
65–69 years
70–74 years
75–79 years
80–84 years≥85 years
Living alone (%)
Missing
History of CVDa or cancer (%)
Missing
Smoking (%)
Current smoker
Quit
Never smoked
Missing
Drinking (%)
Current drinker
Quit
Non-drinker
MissingMarried (%)
Missing
Depression (%)
Missing
Generalized trust (%)
VeryModerately
NeutralSlightly or not at all
Missing
Mutual help (%)
Very
Moderately
NeutralSlightly or not at all
Missing
Community attachment (%)
VeryModerately
NeutralSlightly or not at all
Missing
Sports club activities (%)≥1 time/week
1–2 times/month
A few times/year
Never
Missing
Hobby groups (%)≥1 time/week
1–2 times/month
A few times/year
Never
Missing
Volunteer groups (%)

No damage

Half/partly destroyed

Totally/mostly destroyed

Missing

1,283
11.9
10.8
1.6
6.6
36.5
5.6
46.1
47.0

1,577
13.3
11.9
1.0
6.7
33.4
5.5
45.2
44.6

246
17.5
17.9
2.0
8.1
40.7
6.9
51.6
41.5

938
16.7
2.4
91.5
4.1
68.4
3.3
72.5
47.6

30.5
31.2
20.8
12.0
5.5
9.8
0.9
18.2
25.6

31.9
28.7
23.5
11.0
4.9
7.6
1.2
18.2
23.4

32.1
27.2
21.5
11.4
7.7
4.9
3.3
22.8
23.2

24.8
25.4
21.9
17.2
10.8
10.0
3.3
24.2
23.2

10.5
28.1
55.0
6.3

10.0
25.8
56.8
7.5

13.0
20.7
55.3
11.0

12.6
27.5
50.9
9.1

38.2
3.3
57.1
1.4
72.1
1.6
24.2
9.7

38.9
2.9
56.4
1.8
74.6
1.5
27.3
10.2

29.3
5.3
61.0
4.5
67.5
5.3
29.3
13.0

34.3
4.4
59.3
2.0
65.5
3.3
31.5
13.7

10.6
58.1
24.6
5.1
1.6

13.8
55.1
25.6
4.5
1.0

15.9
51.2
20.7
8.5
3.7

12.4
50.1
28.0
6.8
2.7

5.9
50.9
32.0
9.0
2.3

8.1
50.7
30.2
9.2
1.8

11.0
44.3
28.1
12.2
4.5

9.1
43.1
32.8
10.9
4.2

23.4
55.2
14.2
5.9
1.3

27.9
52.8
14.1
4.4
0.7

37.0
41.1
13.0
6.5
2.4

24.7
45.2
18.7
9.3
2.1

16.8
5.1
4.4
59.9
13.7

19.5
5.9
6.1
55.2
13.3

7.3
3.7
5.3
58.5
25.2

9.9
4.5
3.4
63.3
18.9

19.3
16.0
9.1
43.7
12.0

23.7
16.7
9.3
38.4
11.9

10.2
11.4
10.2
43.9
24.4

14.8
10.3
6.2
51.4
17.3
(Continued on following page)

Int J Public Health | Owned by SSPH+ | Published by Frontiers

4

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

TABLE 1 | (Continued) Characteristics by the level of housing damage among 4,044 men and women aged ≥65 years (Iwanuma, Japan, 2010).

≥1 time/week
1–2 times/month
A few times/year
Never
Missing
Providing emotional support (%)
Missing
Receiving emotional support (%)
Missing
Receiving instrumental support (%)
Missing
Education years (%)<10 years
10–12 years≥13 years
Missing
Current working (%)
Missing
Equivalent income (%)
Q1
Q2
Q3
Q4
Missinga

No damage

Half/partly destroyed

Totally/mostly destroyed

Missing

3.3
6.2
7.1
64.5
18.9
90.6
2.8
89.7
3.6
93.4
1.8

4.0
7.3
9.1
62.1
17.5
91.9
3.4
91.4
3.2
95.8
1.8

2.0
4.5
11.0
54.1
28.5
89.8
4.9
87.4
6.5
93.5
3.3

3.3
3.4
4.7
65.5
23.1
86.8
5.1
82.6
6.9
90.4
3.8

31.3
46.1
21.4
1.3
14.1
9.2

32.7
43.8
22.2
1.3
18.5
9.8

58.9
26.4
12.2
2.4
19.5
16.7

39.8
40.3
17.4
2.6
13.4
15.1

20.1
20.7
22.5
22.1
14.5

19.0
20.1
23.5
21.4
16.0

32.9
17.5
15.0
13.0
21.5

24.0
19.1
18.0
18.3
20.6

CVD, cardiovascular disease.

TABLE 2 | PRsa and 95% CIsa of unhappiness according to the level of housing damage (Iwanuma, Japan, 2010, 2013, 2016, and 2019).
No damage

Half/partly destroyed

Totally/mostly destroyed

1,660

2,033

351

Reference
Reference
Reference
Reference

1.11 (0.94–1.31)
1.11 (0.94–1.31)
1.08 (0.90–1.28)
1.09 (0.91–1.29)

1.51 (1.18–1.94)
1.54 (1.20–1.98)
1.25 (0.99–1.57)
1.18 (0.93–1.48)

Reference
Reference
Reference
Reference

1.05 (0.73–1.51)
1.05 (0.73–1.51)
1.06 (0.72–1.58)
1.07 (0.73–1.58)

1.65 (0.97–2.79)
1.67 (0.98–2.84)
1.42 (0.84–2.42)
1.34 (0.79–2.28)

Reference
Reference
Reference
Reference

0.90 (0.60–1.35)
0.89 (0.59–1.34)
0.97 (0.63–1.51)
0.98 (0.64–1.52)

1.23 (0.64–2.35)
1.23 (0.64–2.39)
1.07 (0.56–2.08)
1.02 (0.53–1.97)

Reference
Reference
Reference
Reference

0.93 (0.60–1.43)
0.92 (0.59–1.44)
0.94 (0.59–1.49)
0.95 (0.60–1.51)

1.14 (0.56–2.34)
1.17 (0.56–2.44)
1.08 (0.53–2.23)
1.03 (0.50–2.12)

No. of participants
Unhappiness in 2010 (%)
Crude PRs (95% CIs)
Sex-age-adjusted PRs (95% CIs)
Multivariate-adjusted PRs1b (95% CIs)
Multivariate-adjusted PRs2c (95% CIs)
Unhappiness in 2013 (%)
Crude PRs (95% CIs)
Sex-age-adjusted PRs (95% CIs)
Multivariate-adjusted PRs1 (95% CIs)
Multivariate-adjusted PRs2 (95% CIs)
Unhappiness in 2016 (%)
Crude PRs (95% CIs)
Sex-age-adjusted PRs (95% CIs)
Multivariate-adjusted PRs1 (95% CIs)
Multivariate-adjusted PRs2 (95% CIs)
Unhappiness in 2019 (%)
Crude PRs (95% CIs)
Sex-age-adjusted PRs (95% CIs)
Multivariate-adjusted PRs1 (95% CIs)
Multivariate-adjusted PRs2 (95% CIs)aPR, prevalence ratio; CI, conﬁdence interval.
Multivariate-adjusted PRs1 were adjusted for sex (men or women), age (65–69 years, 70–74 years, 75–79 years, 80–84 years, or ≥85 years), living alone (yes or no), history of
cardiovascular disease or cancer (yes or no), smoking (never smoked, quit, or current smoker), drinking (non-drinker, quit, or current drinker), marital status (yes or no), depression (yes orno), generalized trust (very, moderately, neutral, or slightly or not at all), mutual help (very, moderately, neutral, or slightly or not at all), community attachment (very, moderately, neutral, or
slightly or not at all), sports club activities (≥1 time/week, 1–2 times/month, a few times/year, or never), hobby groups (≥1 time/week, 1–2 times/month, a few times/year, or never),volunteer groups (≥1 time/week, 1–2 times/month, a few times/year, or never), providing emotional support (yes or no), and receiving emotional support (yes or no), and receiving
instrumental support (yes or no).
cMultivariate-adjusted PRs2 were adjusted for variables in multivariate-adjusted PRs1 plus education level (<10 years, 10–12 years, or ≥13 years), current working (yes or no), andequivalent income (quartile).
b

Int J Public Health | Owned by SSPH+ | Published by Frontiers

5

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

FIGURE 2 | The multivariate-adjusted prevalence of unhappiness according to the level of housing damage in each year (Iwanuma, Japan, 2010, 2013, 2016, and2019). Multivariate-adjusted prevalence was estimated using a mixed model for repeated measures by Poisson distribution with robust error variance adjusted forconfounders.

housing damage than in those with no damage, especially
immediately after the earthquake. However, this difference
progressively diminished during the follow-up.
Irrespective of the level of housing damage, the prevalence of
unhappiness tends to decrease over time. Several studies haveshown that feelings of happiness have a U-shaped association
with age throughout the life course [33–35], which suggests thatolder adults tend to pay more attention to positive information[36, 37]. The decreasing trend in the present results was caused byaging during the follow-up.
The study participants received several forms of public
support, such as free medical care, tax allowance, relief money,
and community formation. In addition, people often rate theirpresent level of happiness compared to the past; thus, the disastermay have lowered the reference point for happiness, andsurvivors may have adjusted to their current circumstances. In
particular, survivors who had experienced housing loss were
initially moved to temporary trailer homes and lived in
cramped conditions for approximately 5 years. By 2016, they
were ﬁnally moved to permanent housing, which likely
contributed to improving their level of happiness. These life
experiences may have caused changes in their lifestyles andsense of value, making it easier for them to perceive theircurrent situation as happy. According to the theory of hedonic
adaptation, both favorable events (e.g., winning the lottery) andunfavorable events (e.g., being paralyzed) result in long-run
adaptation, so that people return to their happiness baseline
over time [38]. Our ﬁndings are consistent with the concept ofhedonic adaptation, i.e., over an 8-year follow-up after thedisaster, the unhappiness levels of people who sufferedhousing damage tended to converge with the levels of those
who had escaped damage.
The earthquake served as a natural experiment. Participants
were randomly exposed; however, the prevalence of unhappiness

Int J Public Health | Owned by SSPH+ | Published by Frontiers

might differ between levels of housing damage before theearthquake. During the earthquake, housing damage wascaused by the tsunami rather than by the tremors or ﬁre. The
magnitude of housing damage was worse in regions close to thecoastline, underscoring the inﬂuence of SES on where people
lived. In fact, SES characteristics, such as education level, current
working, and equivalent income before the earthquake, differed
between levels of housing damage. Hence, the difference in theprevalence of unhappiness between levels of housing damage
before the earthquake is due to the regional speciﬁcs. PRs wereattenuated after adjusting for baseline factors, including SES,
before the disaster. These differences might disappear if
unmeasured SES is adjusted for as well.
Several studies have shown that feelings of happiness areassociated with healthy behaviors [10–15, 17–22, 39–42]. After adisaster, victims tend to experience deteriorated health [43–47] andengage in unhealthy behaviors [48–51], though some of these
conditions (e.g., depression) can be overcome in the long-term[45, 47]. Satisfying subjective happiness in victims might reduce
unhealthy behaviors and contribute to the recovery of their health. A
major strength of the present study is that it is the ﬁrst to examine theassociation between housing damage and happiness. Additionally, in
studies on disasters, data are usually collected after the disaster;
however, the baseline data of this study were collected just before theearthquake. Thus, we can discuss the effects of the disaster byconsidering the status before the disaster without the possibilityof recall bias. In the present results, it can be interpreted that thehigher prevalence of unhappiness in 2013 in the group with the most
severe housing damage was not caused by the earthquake, and thedifference might have existed before the earthquake. Information
about disaster damage is usually affected by survival and response
bias, as only survivors can respond to a questionnaire, and those withsevere damage tend to be non-responsive and lost to follow-up [52].
For this reason, studies on disasters tend to underestimate their

6

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

effects; however, the present study reduced this bias throughmultiple imputation and a mixed model for repeated measures.
Nevertheless, this study has several limitations. First, as discussed
above, the results might be residually biased because of unmeasured
confounders, such as wealth. We acknowledge that the reason for the
pre-disaster difference might be the presence of other characteristics
associated with geographic regions (and, hence, housing damage) andhappiness. However, proximity to the coastline (which approximatesthe geographic location of a household) was strongly correlated withhousing damage. Adjusting for information about location in theanalysis would cause a serious multicollinearity problem. Secondly,
the results should be interpreted with caution due to the highprevalence of missing information about happiness during thefollow-up period. However, with the use of a mixed model for
repeated measures, an unbiased estimator for missing data can beobtained if the pattern of missing data is missing completely at
random or missing at random (MAR) [53, 54]. Meanwhile, we
referred to unmeasured variables elsewhere. In this case, the MAR
assumption cannot be satisﬁed, so the possibility of bias cannot be
ruled out. Third, unhappiness was assessed using only a single
question with binary options, thus affecting reliability. Some of theparticipants were asked the following question: “To what degree doyou feel you are currently happy?” (Score “0” for “Very unhappy” and“10” for “Very happy”). We created a cross-tabulation between thepresent outcome and this question (Supplementary Table S1). Thereseems to be a cutoff between points four and ﬁve, and the present
outcome has a certain validity (Spearman’s rank correlation
coefﬁcient; r = 0.351, p < 0.001). Fourth, our ﬁrst follow-up
survey, including a question about happiness, was conductedalmost 2.5 years after the earthquake. However, although theassociation between the level of housing damage and happiness
immediately after the disaster remains unknown, the aim of thepresent study was to examine the long-term trends in this association,
including those before the disaster.
In conclusion, the present study showed that the long-term
trend in the prevalence of unhappiness in victims of severe
housing damage due to the earthquake decreased until 2019.
Because feelings of happiness are associated with healthy
behaviors, it might be helpful to recover the health status ofdisaster victims.

Chiba University (2493). The patients/participants provided theirwritten informed consent to participate in this study.

AUTHOR CONTRIBUTIONS
MN contributed to the study design and data analysis. JA, KK, and IK
participated in the data collection. MN and JA prepared the initial
draft of the manuscript. All authors participated in the critical revision
and approved the ﬁnal version of the manuscript for submission.

FUNDING
The authors declare that this study received funding from NIH
grants R01 AG042463 and R01 AG042463-06; Grants-in-Aid for
Scientiﬁc Research from the Japan Society for the Promotion of
Science KAKENHI 23243070, KAKENHI 22390400, KAKENHI
24390469, and KAKENHI 19H03860; Health Labour Sciences
Research Grant from the Japanese Ministry of Health, Labour,and Welfare H22-Choju-Shitei-008, H24- Choju-Wakate-009,
19FA1012, and 19FA2001; and grant S0991035 from theStrategic Research Foundation Grant-Aided Project for Private
Universities from the Japanese Ministry of Education, Culture,
Sports, Science, and Technology. The funder was not involved in
the study design, collection, analysis, interpretation of data, thewriting of this article or the decision to submit it for publication.

CONFLICT OF INTEREST
The authors declare that the research was conducted in theabsence of any commercial or ﬁnancial relationships that
could be construed as a potential conﬂict of interest.

ACKNOWLEDGMENTS
We appreciate the support and cooperation of the Iwanuma
mayor’s ofﬁce and the staff of the Department of Health andWelfare of the Iwanuma City Government.

ETHICS STATEMENT
SUPPLEMENTARY MATERIAL
The studies involving human participants were reviewed andapproved by the Research of Human Subjects at the Harvard T.H.
Chan School of Public Health (CR-23143), Tohoku University(21-40, 24-29), Nihon Fukushi University (10-05, 13-14), andThe Supplementary Material for this article can be found online at:https://www.ssph-journal.org/articles/10.3389/ijph.2022.1604901/
full#supplementary-material
3. Lomas T, Case BW, Cratty F, VanderWeele TJ. A Global History of Happiness.
Intnl J Wellbeing (2021) 11:68–87. doi:10.5502/ijw.v11i4.1457
4. Kim J, Song Y, Kim T, Park K. Predictors of Happiness Among Older Korean
Women Living Alone. Geriatr Gerontol Int (2019) 19:352–6. doi:10.1111/ggi.
13615
5. Luchesi BM, de Oliveira NA, de Morais D, de Paula Pessoa RM, Pavarini SCI,
Chagas MHN. Factors Associated with Happiness in the Elderly Persons

REFERENCES
1. Tatarkiewicz W. Analysis of Happiness. Dordrech: Springer (2011).
2. Kazemi M, Honarvar B, Heydari ST, Joulaei H, Haghighi MRR, Lankarani KB.
Happiness in Marginalized Populations: a Community-Based Study in South
Central Iran. BMC Psychol (2021) 9:58. doi:10.1186/s40359-021-00545-2

Int J Public Health | Owned by SSPH+ | Published by Frontiers

7

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

Living in the Community. Arch Gerontol Geriatr (2018) 74:83–7. doi:10.1016/j.
archger.2017.10.006
6. Cooper C, Bebbington P, Livingston G. Cognitive Impairment and Happinessin Old People in Low and Middle Income Countries: Results from the 10/
66 Study. J Affect Disord (2011) 130:198–204. doi:10.1016/j.jad.2010.09.017
7. Jo HK, Kim HK, Jeong JN. Factors Affecting Happiness Among Rural
Residents: A Cross Sectional Survey. Community Ment Health J (2020) 56:
915–24. doi:10.1007/s10597-020-00555-1
8. Hirsch JK, Duberstein PR, Unutzer J. Chronic Medical Problems andDistressful Thoughts of Suicide in Primary Care Patients: Mitigating Roleof Happiness. Int J Geriatr Psychiatry (2009) 24:671–9. doi:10.1002/gps.2174
9. Koivumaa-Honkanen H, Honkanen R, Koskenvuo M, Kaprio J. Self-reported
Happiness in Life and Suicide in Ensuing 20 Years. Soc Psychiatry Psychiatr
Epidemiol (2003) 38:244–8. doi:10.1007/s00127-003-0625-4
10. Liu B, Floud S, Pirie K, Green J, Peto R, Beral V, et al. Does Happiness Itself
Directly Affect Mortality? the Prospective UK Million Women Study. Lancet(2016) 387:874–81. doi:10.1016/S0140-6736(15)01087-9
11. Kye SY, Kwon JH, Park K. Happiness and Health Behaviors in South Korean
Adolescents: a Cross-Sectional Study. Epidemiol Health (2016) 38:e2016022.
doi:10.4178/epih.e2016022
12. Kye SY, Park K. Health-related Determinants of Happiness in Korean Adults.
Int J Public Health (2014) 59:731–8. doi:10.1007/s00038-014-0588-0
13. Kim KH, Chun JH, Sohn HS. The Inﬂuence of Regional Deprivation index on
Personal Happiness Using Multilevel Analysis. Epidemiol Health (2015) 37:
e2015019. doi:10.4178/epih/e2015019
14. Fararouei M, Brown IJ, Akbartabar Toori M, Estakhrian Haghighi R, Jafari J.
Happiness and Health Behaviour in Iranian Adolescent Girls. J Adolesc (2013)36:1187–92. doi:10.1016/j.adolescence.2013.09.006
15. Piqueras JA, Kuhne W, Vera-Villarroel P, van Straten A, Cuijpers P. Happinessand Health Behaviours in Chilean College Students: a Cross-Sectional Survey.
BMC Public Health (2011) 11:443. doi:10.1186/1471-2458-11-443
16. Haghdoost AA, Momeni M, Bahraminejad F, Danaei M. Levels and Predictorsof Happiness in the South of the Islamic Republic of Iran. East Mediterr Health
J (2020) 26:779–86. doi:10.26719/emhj.20.002
17. Pengpid S, Peltzer K. Sedentary Behaviour, Physical Activity and Life
Satisfaction, Happiness and Perceived Health Status in University Students
from 24 Countries. Int J Environ Res Public Health (2019) 16:2084. doi:10.
3390/ijerph16122084
18. Lathia N, Sandstrom GM, Mascolo C, Rentfrow PJ. Happier People Live More
Active Lives: Using Smartphones to Link Happiness and Physical Activity.
PLoS One (2017) 12:e0160589. doi:10.1371/journal.pone.0160589
19. Richards J, Jiang X, Kelly P, Chau J, Bauman A, Ding D. Don’t Worry, Be
Happy: Cross-Sectional Associations between Physical Activity and Happinessin 15 European Countries. BMC Public Health (2015) 15:53. doi:10.1186/
s12889-015-1391-4
20. Khazaee-Pool M, Sadeghi R, Majlessi F, Rahimi Foroushani A, RAhimi
ForoushAni A. Effects of Physical Exercise Programme on HappinessAmong Older People. J Psychiatr Ment Health Nurs (2015) 22:47–57.
doi:10.1111/jpm.12168
21. Wang F, Orpana HM, Morrison H, de Groh M, Dai S, Luo W. Long-term
Association between Leisure-Time Physical Activity and Changes in
Happiness: Analysis of the Prospective National Population Health Survey.
Am J Epidemiol (2012) 176:1095–100. doi:10.1093/aje/kws199
22. Moljord IE, Moksnes UK, Eriksen L, Espnes GA. Stress and Happiness Among
Adolescents with Varying Frequency of Physical Activity. Percept Mot Skills(2011) 113:631–46. doi:10.2466/02.06.10.13.PMS.113.5.631-646
23. National Police Agency of Japan. Police Countermeasures and Damage
Situation Associated with 2011 Tohoku District - off the Paciﬁc Ocean
Earthquake March 10, 2021. Tokyo: National Police Agency of Japan (2021).
24. Kondo K, Rosenberg M. Advancing Universal Health Coverage throughKnowledge Translation for Healthy Ageing: Lessons Learnt from the Japan
Gerontological Evaluation Study. Geneva: World Health Organization (2018).
25. Ishikuro M, Obara T, Murakami K, Ueno F, Noda A, Kikuya M, et al.
Relation of Disaster Exposure with Maternal Characteristics and Obstetric
Outcomes: the Tohoku Medical Megabank Project Birth and ThreeGeneration Cohort Study. J Epidemiol (2021) 2021:JE20210052. doi:10.
2188/jea.JE20210052

Int J Public Health | Owned by SSPH+ | Published by Frontiers

26. Wada T, Ishine M, Kita T, Fujisawa M, Matsubayashi K. Depression Screeningof Elderly Community-Dwelling Japanese. J Am Geriatr Soc (2003) 51:1328–9.
doi:10.1046/j.1532-5415.2003.514207.x
27. Burke WJ, Roccaforte WH, Wengel SP. The Short Form of the GeriatricDepression Scale: a Comparison with the 30-item Form. J Geriatr Psychiatry
Neurol (1991) 4:173–8. doi:10.1177/089198879100400310
28. Murata C, Kondo K, Hirai H, Ichida Y, Ojima T. Association between
Depression and Socio-Economic Status Among Community-Dwelling
Elderly in Japan: the Aichi Gerontological Evaluation Study (AGES).
Health Place (2008) 14:406–14. doi:10.1016/j.healthplace.2007.08.007
29. Schreiner AS, Hayakawa H, Morimoto T, Kakuma T. Screening for Late Life
Depression: Cut-Off Scores for the Geriatric Depression Scale and the Cornell
Scale for Depression in Dementia Among Japanese Subjects. Int J Geriatr
Psychiatry (2003) 18:498–505. doi:10.1002/gps.880
30. Gero K, Hikichi H, Aida J, Kondo K, Kawachi I. Associations between
Community Social Capital and Preservation of Functional Capacity in the
Aftermath of a Major Disaster. Am J Epidemiol (2020) 189:1369–78. doi:10.
1093/aje/kwaa085
31. Spiegelman D, Hertzmark E. Easy SAS Calculations for Risk or Prevalence
Ratios and Differences. Am J Epidemiol (2005) 162:199–200. doi:10.1093/aje/
kwi18832. Rubin DB, Schenker N. Multiple Imputation for Interval Estimation from
Surveys with Ignorable Nonresponse. J Am Stat Assoc (1985) 81:366–74.
33. Shimai S, Yamamiya Y, Fukuda S. Subjective Happiness Among Japanese
Adults: An Upward Tendency Associated with Age. Nihon Koshu Eisei Zasshi(2018) 65:553–62. doi:10.11236/jph.65.9_553
34. Blanchﬂower DG. Is Happiness U-Shaped Everywhere? Age and Subjective
Well-Being in 145 Countries. J Popul Econ (2020) 34:575–624. doi:10.1007/
s00148-020-00797-z
35. Blanchﬂower DG, Oswald AJ. Is Well-Being U-Shaped over the Life Cycle? Soc
Sci Med (2008) 66:1733–49. doi:10.1016/j.socscimed.2008.01.030
36. Charles ST, Mather M, Carstensen LL. Aging and Emotional Memory: theForgettable Nature of Negative Images for Older Adults. J Exp Psychol Gen(2003) 132:310–24. doi:10.1037/0096-3445.132.2.310
37. Laura LC, Joseph AM. At the Intersection of Emotion and Cognition: Agingand the Positivity Effect. Curr Dir Psychol Sci (2005) 14:117–21. doi:10.1111/j.
0963-7214.2005.00348.x
38. Brickman P, Coates D, Janoff-Bulman R. Lottery Winners and Accident
Victims: Is Happiness Relative? J Pers Soc Psychol (1978) 36:917–27. doi:10.
1037//0022-3514.36.8.917
39. Felez-Nobrega M, Olaya B, Haro JM, Stubbs B, Smith L, Koyanagi A.
Associations between Sedentary Behavior and Happiness: An Analysis ofInﬂuential Factors Among Middle-Aged and Older Adults from Six Lowand Middle-Income Countries. Maturitas (2021) 143:157–64. doi:10.1016/j.
maturitas.2020.10.011
40. Engstrom G, Henningsohn L, Steineck G, Leppert J. Self-assessed Health,Sadness and Happiness in Relation to the Total burden of Symptoms from theLower Urinary Tract. BJU Int (2005) 95:810–5. doi:10.1111/j.1464-410X.2005.
05406.x
41. Zhao SZ, Wang MP, Viswanath K, Lai A, Fong DYT, Lin CC, et al. Short Sleep
Duration and Insomnia Symptoms Were Associated with Lower Happiness
Levels in Chinese Adults in Hong Kong. Int J Environ Res Public Health (2019)16:2079. doi:10.3390/ijerph16122079
42. Otsuka Y, Kaneita Y, Itani O, Jike M, Osaki Y, Higuchi S, et al. The
Relationship between Subjective Happiness and Sleep Problems in Japanese
Adolescents. Sleep Med (2020) 69:120–6. doi:10.1016/j.sleep.2020.01.008
43. Orui M, Harada S, Saeki S, Sato K, Kozakai Y, Hayashi M. Trend in the SuicideRate Following the Great East Japan Earthquake: an Eight-Year Follow-Up
Study in Miyagi Prefecture. Seishin Shinkeigaku Zasshi (2020) 122:573–84. (inJapanese).
44. Matsubayashi T, Sawada Y, Ueda M. Natural Disasters and Suicide: Evidence
from Japan. Soc Sci Med (2013) 82:126–33. doi:10.1016/j.socscimed.2012.
12.021
45. Oe M, Maeda M, Nagai M, Yasumura S, Yabe H, Suzuki Y, et al. Predictors of
Severe Psychological Distress Trajectory after Nuclear Disaster: Evidence fromthe Fukushima Health Management Survey. BMJ Open (2016) 6:e013400.
doi:10.1136/bmjopen-2016-013400

8

September 2022 | Volume 67 | Article 1604901

Nagai et al.

Happiness Trend in Disaster Damage

51. Yagi A, Maeda M, Suzuki Y, Yabe H, Yasumura S, Niwa S, et al. Changes in
Drinking Behavior Among Evacuees after the Fukushima Daiichi Nuclear
Power Plant Accident: the Fukushima Health Management Survey. Fukushima
J Med Sci (2020) 66:133–42. doi:10.5387/fms.2019-24
52. Shiba K, Kawahara T, Aida J, Kondo K, Kondo N, James P, et al. Causal
Inference in Studying the Long-Term Health Effects of Disasters: Challenges
and Potential Solutions. Am J Epidemiol (2021) 190:1867–81. doi:10.1093/aje/
kwab064
53. Bell ML, Rabe BA. The Mixed Model for Repeated Measures for Cluster Randomized
Trials: a Simulation Study Investigating Bias and Type I Error with Missing
Continuous Data. Trials (2020) 21:148. doi:10.1186/s13063-020-4114-9
54. Ashbeck EL, Bell ML. Single Time point Comparisons in LongitudinalRandomized Controlled Trials: Power and Bias in the Presence of Missing
Data. BMC Med Res Methodol (2016) 16:43. doi:10.1186/s12874-016-0144-0

46. Tomata Y, Kakizaki M, Suzuki Y, Hashimoto S, Kawado M, Tsuji I. Impact ofthe 2011 Great East Japan Earthquake and Tsunami on Functional Disability
Among Older People: a Longitudinal Comparison of Disability Prevalence
Among Japanese Municipalities. J Epidemiol Community Health (2014) 68:
530–3. doi:10.1136/jech-2013-203541
47. Ohira T, Nakano H, Okazaki K, Hayashi F, Yumiya Y, Sakai A. Trends in
Lifestyle-Related Diseases before and after the Great East Japan Earthquake:
the Fukushima Health Management Survey. J Natl Inst Public Health (2018)
67:34–41.
48. Nagai M, Ohira T, Yasumura S, Takahashi H, Yuki M, Nakano H, et al.
Association between Evacuation Condition and Habitual Physical Activity inGreat East Japan Earthquake Evacuees: The Fukushima Health Management
Survey. Nihon Koshu Eisei Zasshi (2016) 63:3–10. (in Japanese). doi:10.11236/
jph.63.1_3
49. Zhang W, Ohira T, Abe M, Kamiya K, Yamashita S, Yasumura S, et al.
Evacuation after the Great East Japan Earthquake Was Associated with Poor
Dietary Intake: The Fukushima Health Management Survey. J Epidemiol(2017) 27:14–23. doi:10.1016/j.je.2016.08.002
50. Ma E, Ohira T, Nakano H, Maeda M, Yabe H, Sakai A, et al. Dietary Pattern
Changes in Fukushima Residents after the Great East Japan Earthquake: theFukushima Health Management Survey 2011-2013. Public Health Nutr (2021)
24:2195–204. doi:10.1017/S1368980020000300

Int J Public Health | Owned by SSPH+ | Published by Frontiers

Copyright © 2022 Nagai, Hikichi, Shiba, Kondo, Kawachi and Aida. This is an openaccess article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that theoriginal publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.

9September 2022 | Volume 67 | Article 1604901