Social Science & Medicine 264 (2020) 113385

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Social Science & Medicine
journal homepage: http://www.elsevier.com/locate/socscimed

Causal effect of deteriorating socioeconomic circumstances on new-onset
arthritis and the moderating role of access to medical care: A naturalexperiment from the 2011 great east Japan earthquake and tsunami
Takaaki Ikeda a, b, *, Jun Aida c, d, Ichiro Kawachi e, Katsunori Kondo f, g, Ken Osaka b
aDepartment of Health Policy Science, Graduate School of Medical Science, Yamagata University, Yamagata, Japan
Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Miyagi, Japan
Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japand
Division for Regional Community Development, Liaison Center for Innovative Dentistry, Graduate School of Dentistry, Tohoku University, Miyagi, Japane
Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA, USAf
Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University, Chiba, Japang
Department of Gerontological Evaluation, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Aichi, Japanbc

A R T I C L E I N F O

A B S T R A C T

Keywords:
Arthrosis
Instrumental variable models
IV
Natural disasterNatural experiment
Orthopedics
Osteoarthritis
Socioeconomic status

Socioeconomic disadvantage is a risk factor for arthritis, but its causal relationship remains unclear. This study
examined the causal relationship between socioeconomic circumstances and new-onset arthritis by taking
advantage of the “natural experiment” that resulted from the Great East Japan Earthquake and Tsunami. The
baseline survey was conducted in August 2010, 7 months before the disaster. Self-reported questionnaires were
mailed to all eligible residents of Iwanuma City in Miyagi Prefecture. The earthquake and tsunami occurred on
March 11, 2011. The follow-up survey was conducted in October 2013, as well as the gathering of information
about disaster damage (housing damage and subjective deterioration of economic circumstances) and healthrelated information. We used a two-stage least squares instrumental variable model to analyze 2360 survivors
who did not have arthrosis at baseline, of whom 95 (4.0%) developed arthritis over the 2.5-year follow-up
period. We used the linear probability model for the estimations. Our results revealed that both the subjective
deterioration of economic circumstances and housing damage were associated with the development of arthritis(95% confidence interval [CI], 0.08 [0.03–0.12] and 0.02 [0.01–0.04], respectively). In addition, we also foundthat the disruption of access to orthopedics was associated with the development of arthritis. Our findings addedrobust evidence of the causal relationship between worsening economic circumstances and the development ofarthritis. Our study emphasized the importance of recovery as well as the establishment of the post-disasterorthopedic medical system in the aftermath of a disaster.

1. Introduction
Arthritis is a degenerative disease that causes pain and inflammationin the joints; it is also known as one of the leading causes of disability(Theis et al., 2018). In the USA, 54.4 million adults, approximately
one-quarter of all adults, are affected by this disease (Barbour et al.,2017). With the increase in the age of the population, the prevalence ofarthritis is expected to increase significantly by 2040, with as many as
78 million US adults expected to be diagnosed with arthritis.
Several previous studies have reported that socioeconomic status(SES) is associated with arthritis, and that people with a lower SES
tended to suffer more from this disease (Bengtsson et al., 2005; Cleve­land et al., 2013; Leigh and Fries, 1991). In general, individuals withlower SES are more likely to engage in heavy labor, which is considered
as one of the primary risk factors for arthritis (Mehlum et al., 2008;
O’Reilly et al., 2000). In addition, these individuals are more likely tosuffer from previous injuries, which also increases the risk for subse­
quent arthritis (Felson, 1994). Moreover, obesity, which is associatedwith SES (McLaren, 2007; Shiba et al., 2019; Wardle et al., 2002), hasalso been reported as a common risk factor for arthritis (Chaganti and* Corresponding author. Department of Health Policy Science, Graduate School of Medical Science, Yamagata University, Yamagata, Japan, 2-2-2 Iidanishi,
Yamagata, Yamagata, 990-9585, Japan.
E-mail address: t.ikeda0110@gmail.com (T. Ikeda).
https://doi.org/10.1016/j.socscimed.2020.113385
Received in revised form 1 September 2020; Accepted 18 September 2020
Available online 25 September 20200277-9536/© 2020 The Authors.
Published by Elsevier Ltd.
(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Social Science & Medicine 264 (2020) 113385

Lane, 2011; Daïen and Sellam, 2015).
However, there are limited studies on the causal relationship be­
tween SES and the new-onset arthritis because of the inherent nature ofobservational studies (Bengtsson et al., 2005; Cleveland et al., 2013;
Leigh and Fries, 1991). To overcome this limitation, the instrumental
variable (IV) model enabled us to mimic randomized control trials in
observational studies (Greenland, 2000). This model can provide arobust causal relationship by applying an IV, which is only associatedwith the exposure and only affects the outcome via the exposure (Davies
et al., 2013).
Thus far, earlier works have examined the association between
health status and natural disasters, such as earthquake, Tsunami, andhurricane (Aida et al., 2017; Fergusson et al., 2014; Hikichi et al., 2016;
Matsuyama et al., 2017; Tsuboya et al., 2016; Wilson-Genderson et al.,2018). These studies took advantage of these so-called natural experi­ments (Craig et al., 2012), in which data on health conditions both before
and after the disasters were available (Aida et al., 2017; Fergusson et al.,2014; Hikichi et al., 2016; Matsuyama et al., 2017; Tsuboya et al., 2016;
Wilson-Genderson et al., 2018). Among them, two studies haveattempted to elucidate causal relationships between disaster damage
and various health conditions in the survivors of the Great East Japan
Earthquake and Tsunami by using IV models (IV as the distance from thecoastlines) (Hikichi et al., 2016; Matsuyama et al., 2017). Following
these studies, we sought to investigate the causal relationship between
socioeconomic circumstances and the development of arthritis in thesurvivors of the Great East Japan Earthquake and Tsunami using the IVmodel.
2. Methods
2.1. Data
We used repeatedly measured health and SES from the Iwanuma
Study, a part of the Japan Gerontological Evaluation Study (JAGES),
which is a large-scale prospective cohort study that aimed to evaluate
the health of older adults in Japan (Kondo, 2016; Kondo et al., 2018).
We used data from one of the research field sites of the JAGES: Iwanuma
City, on the northeastern coast of Japan. The baseline survey was con­ducted in August 2010, 7 months before the disaster, and self-reported
questionnaires were mailed to all eligible residents of the city. On
March 11, 2011, Iwanuma was approximately 80 km west of theearthquake epicenter, and the disaster killed 186 people and destroyed
5428 houses (Miyagi Prefectural Government, 2019). The follow-up
survey was conducted in October 2013 (31 months after the disaster),and the investigators visited the survivors’ homes in order to collectinformation about the disaster damage, as well as health-related infor­
mation. We included participants who responded to both the baseline
and follow-up surveys and who did not have arthrosis at baseline. We
excluded participants who lacked information on arthrosis at baseline.
Consequently, 2360 participants were included in our final analysis(Fig. 1).

Fig. 1. Recruitment and follow-up diagram (n = 2360).

situation affected by the disaster?,” with possible answers of “became
worse,” “became partially worse,” “no change,” “partially improved,” or“improved.” Those who responded “no change,” “partially improved,”
and “improved” were grouped together in accordance with a previous
study (Matsuyama et al., 2017). Housing damage was determined by
local government inspectors (an objective indicator used for the survi­
vors’ compensation). Housing damage was evaluated by asking, “Whatwas the formal classification of the damage to your house?” withpossible answers of “completely destroyed,” “mostly destroyed,”“half-destroyed,” “partially destroyed,” or “no damage.”
2.4. Covariates and potential mediators
We used several covariates as follows: age (65–69, 70–74, 75–79,
80–84, or ≥85 years); sex; SES (educational attainment and equivalized
household income); body mass index (BMI) (<18.5 kg/m2, 18.5–24.9
kg/m2, 25.0–29.9 kg/m2, and ≥30 kg/m2); any self-reported comor­
bidities for osteoporosis, traumatic injuries, or fractures; smoking status(never, former, or current); and depressive symptoms assessed by theJapanese short version of the Geriatric Depression Scale (≤4, 5–9, ≥10)
(Wada T, Ishine M, Kita T, Fujisawa M, 2003). All of the previously
mentioned covariates were assessed at baseline, i.e., before the disaster.
We also considered the following follow-up variables as potential
mediators: loss of loved ones as a result of the disaster, psychologicaldistress, and disruption of access to orthopedics at follow-up. Thechange in BMI between the baseline and follow-up was also considered
as a potential mediator.
Psychological distress was assessed using the truncated version of the

2.2. Dependent variable: new-onset arthritis
Information on the new-onset arthritis was collected by using selfreported questionnaire in the 2013 follow-up survey, which included
the following question intended for the participants: “Circle all the ill­
nesses or injuries you acquired after the earthquake.” We identified
participants with incident arthritis if they chose “Arthritis” out of the 24
items listed.
2.3. Exposures: disaster damage
We used two types of disaster damage: subjective deterioration of
economic circumstances and housing damage. Subjective deteriorationof economic circumstances was assessed by asking, “Was your economic2

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Social Science & Medicine 264 (2020) 113385

Kessler Psychological Distress Scale, and the participants were grouped
into the following three groups: none, <5; moderate, 5–12; and severe,≥13 (Kessler et al., 2002). Disruption of access to orthopedics due to thedisaster was assessed by asking, “Did you experience any disruption ofaccess to health care? Circle all that apply.” We identified disrupted
participants if they chose “Orthopedics” out of the six possible answers.

the exposure variable. Subjective deterioration of economic circum­
stances was significantly associated with new-onset arthritis in both the
OLS and the IV models of the coefficient (95% confidence interval [CI],0.02 [0.001–0.03] in the OLS model and 0.08 [0.03–0.12] in the IVmodel) (Table 2). The result of our IV model indicates that people whoreported “became worse” for their subjective economic circumstances
had a 16% increase in the probability of developing arthritis comparedwith those who reported “no change/improved.”
Similarly, housing damage was significantly associated with thedevelopment of arthritis in both the OLS and IV models (95% CI, 0.01
(0.002–0.02) in the OLS model and 0.02 (0.01–0.04) in the IV model)(Table 3). The result of our IV model indicates that people who expe­
rienced total home destruction had an 8% increase in the probability ofdeveloping arthritis compared with those who did not experience home
destruction.
For the sensitivity analyses, the loss of loved ones in the disaster,
psychological distress after the disaster, and change in BMI between
baseline and follow-up were not significantly associated with new-onset
arthritis (Tables 4 and 5). However, disruption of access to orthopedics
after the disaster was associated with new-onset arthritis in both the OLS
and IV models. The association between disaster damage and thedevelopment of arthritis was somewhat attenuated but remained sig­
nificant in our IV model (Tables 4 and 5).

2.5. Statistical analyses
We performed a descriptive analysis of the participants’ character­istics, exposures, and outcome. Then, two types of regression analysis
were performed. The exposure variables were added separately to theregression models to avoid over-adjustment bias (Schisterman et al.,2009). We first performed the “conventional” ordinary least squares(OLS) regression with robust standard errors, which is represented asfollows:
Yi = α0 + α1 Disaster Damagei + θCovariatesi + εi
,where Y denotes the outcome of the interest; Disaster Damage, the ex­posures (subjective deterioration of economic circumstances or housing
damage); Covariates, the set of covariates; and ε, the error term.
We then applied the two-stage least squares IV model with robust
standard errors. We used distance from the coastline as an IV, in linewith previous studies (Hikichi et al., 2016; Matsuyama et al., 2017). IVs
must meet the following three conditions: 1) associated with exposureassignment (relevance); 2) not directly associated with the outcome andonly associated with the outcome through exposure (exclusion restric­tion); and 3) independent of any of the covariates (Davies et al., 2013).
The distance from the coastline to each resident’s address at baseline
was assessed using the geographic information systems and was used as
an IV for the disaster damage. Thus, our IV model can be written as
follows:

4. Discussion
To the best of our knowledge, this study is the first to examine thecausal relationship between disaster damage and the new-onset
arthritis. Our results revealed significant associations between subjec­
tive deterioration of economic circumstances and housing damage andthe development of arthritis.
Several previous observational studies have reported that lower SES(educational attainment or income level) is associated with the newonset arthritis (Bengtsson et al., 2005; Cleveland et al., 2013; Leighand Fries, 1991), which is in line with the results of the current study.
However, these studies were observational studies, and there is room forcausal inference. However, we succesfully added robust evidence thatworsened the economic circumstances that occurred as a result of
disaster damage and caused arthritis by applying the IV model, where it
was possible to adjust unmeasured confounders. In addition, previous
studies have reported the causal relationship between disaster damage
and health outcomes, such as dementia (Hikichi et al., 2016) and oral
health (Matsuyama et al., 2017), using the IV model, the results of whichwere consistent with those of the current study. Moreover, lower SES
occupations was also reported to be a risk for arthritis (Cleveland et al.,2013; Leigh and Fries, 1991), that is, those individuals with lower SES
occupations are more likely to suffer from previous injuries, possibly
leading to subsequent arthritis (Felson, 1994). However, since the risk of
injury/heavy physical workload is temporally downstream of working
in lower SES occupations (e.g. manual labor), these factors are notconsidered to be confounders of the association between economic cir­
cumstances and risk of new-onset arthritis. In addition, most of ourparticipants were retired at the time of the baseline survey, and were
therefore unlikely to change their jobs during follow-up.
The possible mechanisms underlying the association between wors­
ening economic circumstances and the development of arthritis are as
follows: 1) changes in health-related behaviors (e.g., healthcare utili­zation and/or overweight/obesity) and 2) psychosocial distress. Indeed,
we found that the disruption of access to orthopedics was associatedwith the new-onset arthritis and somewhat attenuated the association
between worsened economic circumstances and the development ofarthritis. This result suggests that the disruption of access to orthopedics
acts a mediator between economic circumstances and arthritis. An
observational study from the Netherlands reported that lower educa­
tional level was associated with allied healthcare usage among patientswith rheumatoid arthritis, but not with other healthcare services, such

Disaster Damagei = γ0 + γ 1 Distancei + δCovariatesi + τi
̂Damagei + ϕCovariatesi + εiYi = β0 + β1 Disaster
The first equation is the first stage of our regression model in which
we predicted disaster damage (exposures) using our IV (distance fromthe coastline) and the set of covariates. The second equation represents
the second stage of our regression model. In the second stage, we
regressed our outcome of the interest (new-onset arthritis) on the pre­
̂Damagei from the first stage regression and thedicted value of Disasterset of covariates. We applied linear probability models in both the firstand second stage equations on the basis of the recommendation ofprevious studies (Angrist and Pischke, 2008; Matsuyama et al., 2019).
The strength of the instruments was assessed using an F-test of the joint
contribution of the two instruments in predicting treatment, whereby
F-statistics > 10 indicate that instruments are sufficiently strong (Angristand Pischke, 2008). For sensitivity analysis, we conducted the same
analysis in which we added the potential mediators and checked the
reduction in coefficients by the mediators. All the missing variables in
the covariates were turned into dummy variables and added into themodels. All analyses were conducted using Stata, version 16.0 (Stata­
Corp LLC, College Station, Texas).
3. Results
Table 1 presents the characteristics and health status of the study
participants. Among the eligible participants (n = 2360), 95 developed
arthritis after the disaster (4.0%). The participants who had economic
difficulty or housing damage were more likely to develop arthritis.
Tables 2 and 3 present the results of our OLS and IV models. The Fstatistic was 197.7 for the analysis of the subjective deterioration of
economic circumstances and 733.0 for the analysis of housing damage;these values suggest a sufficient correlation between IV regression and3

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Social Science & Medicine 264 (2020) 113385

Table 1
Demographics of participants and the development of arthritis after the 2011 Great East Japan Earthquake and Tsunami (n = 2360), Iwanuma, Japan, 2010–2013.
Total number of participants

P-valuebNew-onset arthritis
No

Yes

n

%

n

%

Subjective deterioration of economic circumstances
No change/improved
Became partially worse
Became worse
Missing

1753
389
167
51

1693
363
158
51

76.5
16.4
7.1
–

60
26
9
0

63.2
27.4
9.5
–

<0.001

Housing damage
No damagePartialMinor
Major
Destroyed
Missing

940
981
176
95
104
64

912
946
156
87
102
62

41.4
42.9
7.1
4.0
4.6
–

28
35
20
8
2
2

30.1
37.6
21.5
8.6
2.2
–

<0.001

Age group (years)
65–69
70–74
75–79
80–84
85–

695
677
519
315
154

665
650
499
301
150

29.4
28.7
22.0
13.3
6.6

30
27
20
14
4

31.6
28.4
21.1
14.7
4.2

0.91

Sex
Men
Women

1057
1303

1030
1235

45.5
54.5

27
68

28.4
71.6

0.002

Educational attainment
≤9

0.49

815

780

34.4

35

36.8

10–12
≥13

986
472

950
454

41.9
20.0

36
18

37.9
19.0

Missing

87

81

3.6

6

6.3

Tertile of pre-disaster income level
Low
MiddleHigh
Missing

705
621
611
423

685
591
586
403

30.2
26.1
25.9
17.8

20
30
25
20

21.1
31.6
26.3
21.1

0.24

Osteoporosis/traumatic injuries/fractures before disaster
No
Yes

2184
176

2098
167

92.6
7.4

86
9

90.5
9.5

0.45

Body mass index before disaster<18.5
18.5–24.9
25.0–29.9
≥30.0

108
1452
566
59

101
1401
541
56

4.5
61.9
23.9
2.5

7
51
25
3

7.4
53.7
26.3
3.2

0.46

175

166

7.3

9

9.5

Smoking status before disaster
Never
Quit
Current
Missing

1302
641
219
198

1242
625
2109.31888.3

54.8
27.6

60
16
9
10

63.2
16.8
9.5
10.5

0.14

Depressive symptoms before disaster
None
Moderate
SevereMissing

1370
488
184
318

1323
463
171
308

58.4
20.4
7.6
13.6

47
25
13
10

49.5
26.3
13.7
10.5

0.05

Loss of loved ones in disasterLoss
No loss

611
1749

579
1686

25.6
74.4

32
63

33.7
66.3

0.08

Disruption of access to orthopedics after disaster
No
Yes

2311
49

2226
39

98.3
1.7

85
10

89.5
10.5

<0.001

Psychological distress after disaster
None
Moderate

1299
842

1258
798

55.5
35.2

41
44

43.2
46.3

0.05

Missing(continued on next page)4

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Social Science & Medicine 264 (2020) 113385

Table 1 (continued )Total number of participants

No
SevereChange in body mass index between baseline and follow-up

P-valuebNew-onset arthritis
Yes

n

%

n

%

219

209

9.2

10

10.5

2126

2043

− 0.11a83− 0.09a–

Because of rounding, percentages do not add up to exactly 100%.
aMean values are represented.
b
Chi-squared test or Fisher’s exact test was performed.

as access to rheumatologists or to general practitioners (Jacobi et al.,2003). The difference between studies may be explained by the differ­ence in the study design, in that our study was a so-called naturalexperiment that was conducted by chance due to the emergency situa­tion of the earthquake, whereas the previous study was conducted innormal times that were unaffected by disaster (Jacobi et al., 2003). In
normal situations, access to health care is supposed to be correlated with
SES; however, following a major disaster, access to health care is dis­
rupted by disaster rather than socioeconomic conditions. Furthermore, anatural experiment observational study from Japan also reported thatthe disruption of access to psychiatry was associated with poor mental
health in the aftermath of the disaster (Tsuboya et al., 2016), which is inagreement with the results of the current study.
Surprisingly, body mass index at baseline was not significantly
associated with the development of arthritis. In addition, changes in BMI
between baseline and follow-up were also not associated with the newonset arthritis. A previous longitudinal study from Japan reported that
housing damage after the earthquake and tsunami was associated withobesity (Shiba et al., 2019). Thus, our results were inconsistent with theprevious studies that indicated that obesity is a risk factor for arthritis(Chaganti and Lane, 2011; Daïen and Sellam, 2015). This discrepancy
may be explained by the fact that participants with deteriorating eco­nomic circumstances due to the disaster were more likely to suffer froman injury on the day of the disaster because of the disruption of access toorthopedics. Post-traumatic arthritis is considered to be a condition
triggered by an acute joint injury that possibly causes choronic arthritis(Punzi et al., 2016). Moreover, it is also considered that treatment for
inflammation occurring immediately after joint injury plays an impor­tant role in preventing the development chronic arthritis (Punzi et al.,2016). Therefore, it is possible that participants with deteriorating
economic circumstances caused by the disaster were more likely torefrain from visiting orthopedics for an injury on the day of the disaster;
this may lead to an accelerated development of arthritis in the affected
limbs rather than the body mass index itself. In that sense, we believethat it is important to establish a medical system immediately after thedisaster for those with joint injuries to prevent future new-onset
arthritis. Unfortunately, we could not obtain the actual information
about the injury on the day of the disaster. Hence, future studies withmore detailed information on somatic diseases are expected to investi­
gate this possibility.
Besides, loss of loved ones in the disaster and psychological distresswere not significantly associated with the development of arthritis in thecurrent study. These results were partially consistent with the previous
studies conducted after the earthquake, which demonstrated that loss of
loved ones in the disaster was not associated with depressive symptoms(Tsuboya et al., 2016), incident dementia (Hikichi et al., 2016), or tooth
loss (Matsuyama et al., 2017). However, psychological distress was
associated with the new-onset arthritis (measured by the self-reported
questionnaire) in the UK general population (McLachlan and Gale,2018). Moreover, two observational studies also reported that depres­sion or anxiety increased the risk of incident arthritis (Patten et al.,2008; Scott et al., 2011). These results were inconsistent with those of
the current study. This discrepancy might be explained by the results

obtained from “conventional” regression models in previous studies(McLachlan and Gale, 2018; Patten et al., 2008; Scott et al., 2011),
which were likely to be biased by unmeasured confounders or reverse
causation. Psychological distress is considered to affect immune func­tion and to increase the level of inflammation (Segerstrom and Miller,
2004; Wium-Andersen et al., 2013), whereas arthritis, in general, isassociated with a higher level of inflammation (Jin et al., 2015).
Moreover, the higher level of inflammation was associated with pain
(Jin et al., 2015); thus, it is possible that pain was caused by inflam­mation due to arthritis, which was considered to be the primary cause of
the reported psychological distress. However, future studies are ex­pected to examine the possible mechanism for the relationship between
psychological distress and arthritis as we did not assess the immune
markers. By integrating these findings, the present study suggests that
ensuring access to orthopedic care is the most important in preventing
the new-onset arthritis.
When we consider the coefficient for the association between sub­jective deterioration in economic circumstances and the new-onset
arthritis, we believe that the coefficient of 0.08 in our IV model iscrucial due to the following reasons: the probability of the developmentof arthritis was 16% higher in participants who reported “became
worse” for their subjective economic circumstances, and this magnitude
is sufficiently large among the variables included in our regression
model. In addition, we also found housing to be important. Indeed, thecoefficient of 0.02 was comparable to the magnitude of associations
between the other variables and the new-onset arthritis because theprobability of developing arthritis was 8% higher in participants who
experienced total home destruction, which is the highest among thevariables included in the current model.
The current study has several strengths and limitations that should
be noted. First, with regard to the strengths, we had general and healthdata of survivors prior to the disaster. Second, the nature of the “naturalexperiment” enabled us to mimic a RCT and infer a causal relationshipbetween economic circumstances and the new-onset arthritis. Our study
also had several limitations that warrant consideration. First, we used
the self-reported questionnaire to detect the new-onset arthritis, and did
not use doctor-diagnosed arthritis development, which may haveresulted in potential misclassification. In addition, we could not identify
the detailed type of the disorder (i.e., osteoarthritis or rheumatoid
arthritis). However, we believe that the accuracy of our outcome isacceptable, given that a systematic review of the validity of self-reported
arthritis (compared to clinical diagnosis) found that the pooled sensi­
tivity and specificity was 0.71 and 0.79, respectively (Peeters et al.,2015). Thus, misclassification of the outcome is likely to have been
non-differential (the sensitivity/specificity of self-report suggests thatpeople are as likely to misreport not having arthritis as falsely reportingthat they have the disease). This type of misclassification is unlikely to
induce bias in the association between exposure and outcome (Rothman
et al., 2008). Second, there is a possibility that the question regarding
the disruption of access to orthopedics may have resulted in an under­
estimation; i.e., it is possible that some participants with no complaints
did not circle the answer even though their access was actually dis­
rupted. However, we believe that we were able to collect, with some5

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Social Science & Medicine 264 (2020) 113385

Table 2
Subjective deterioration of economic circumstances and the development ofarthritis (n = 2309)a, Iwanuma, Japan, 2010–2013.
IV modelb

OLS (without IV)
Subjective
deterioration of
economic
circumstances

Coef.

95% CI

0.02

0.001

0.03

Coef.

95% CI

0.08

0.03

0.12

Reference
0.00− 0.010.00− 0.02− 0.02− 0.03− 0.03− 0.05

0.02
0.02
0.03
0.01

Reference
0.00
0.00
0.01
− 0.01− 0.02− 0.02− 0.02− 0.04

0.02
0.03
0.04
0.02

Sex
Men
Women

Reference
0.79

0.12

1.45

0.03

0.01

0.06

Reference

Reference

10–12
≥ 13

− 0.010.00− 0.03− 0.03

0.01
0.02

0.00
0.01

− 0.02− 0.020.02
0.03

Missing

0.03− 0.040.09

0.03− 0.040.09

Tertile of predisaster income
level
Low
MiddleHigh
Missing
Osteoporosis/
traumatic
injuries/fractures
before disasterNo
Yes
Body mass index
before disaster
18.5–24.9<18.5
25.0–29.9≥ 30.0Missing
Smoking status
before disaster
Never
Quit
Current
Missing
Depressive
symptoms before
disaster
None
Moderate
SevereMissing

Reference
0.03
0.02
0.02

Reference
0.00

0.01
0.002
− 0.01− 0.04

Reference
0.03
0.01
0.01

− 0.02− 0.01− 0.050.01− 0.03

Reference
0.00
0.01
0.00

Reference
0.02
0.03− 0.01− 0.02− 0.02− 0.04− 0.01− 0.01− 0.03

0.05
0.04
0.04

0.03

Reference
0.04
0.03
0.02

Reference− 0.010.02
0.01− 0.01− 0.04

0.06
0.06
0.05

0.03

0.08
0.03
0.07

Reference
0.03
0.01
0.02

− 0.02− 0.01− 0.04

0.08
0.03
0.08

0.05

0.01

− 0.03

0.05

0.02
0.04
0.04

0.04
0.07
0.01

Reference
0.00
0.02
0.00

Reference
0.01
0.02− 0.01

IV modelb

OLS (without IV)

Age group (years)
65–69
70–74
75–79
80–84
85–

Educational
attainment≤9

Table 3
Housing damage and the development of arthritis (n = 2296)a, Iwanuma, Japan,
2010–2013.

− 0.02− 0.02− 0.04− 0.01− 0.02− 0.04

Coef.

95% CI

Coef.

95% CI

Housing damage

0.01

0.002

0.02

0.02

0.01

0.04

Age group (years)
65–69
70–74
75–79
80–84
85–

Reference
0.00− 0.010.00− 0.02− 0.02− 0.03− 0.03− 0.05

0.02
0.02
0.03
0.01

Reference
0.00
0.00
0.00
− 0.02− 0.02− 0.03− 0.03− 0.05

0.02
0.02
0.03
0.01

Sex
Men
Women

Reference
0.03

0.005

0.05

Reference
0.03

0.01

0.05

Educational
attainment≤9

Reference
0.00
0.00

− 0.03− 0.02

0.02
0.02

0.00
0.00

− 0.02− 0.020.02
0.03

Missing

0.03− 0.040.09

0.03− 0.040.09

Tertile of predisaster income
level
Low
MiddleHigh
Missing

Reference
0.03
0.02
0.02

0.01
0.001
− 0.01

0.05
0.04
0.04

Reference
0.03
0.02
0.02

0.01
0.002
− 0.01

0.05
0.04
0.04

Osteoporosis/
traumatic
injuries/
fractures
before disasterNo
Yes

Reference
0.00− 0.04

0.04

Reference
0.00− 0.04

0.04

Body mass index
before disaster
18.5–24.9<18.5
25.0–29.9≥30.0

Reference
0.03
0.01
0.01

− 0.02− 0.01− 0.05

0.08
0.03
0.07

Reference
0.03
0.01
0.01

− 0.02− 0.01− 0.05

0.08
0.03
0.07

0.01

− 0.030.05

0.01− 0.030.05

Smoking status
before disaster
Never
Quit
Current
Missing

Reference
0.00
0.01
0.00

− 0.02− 0.02− 0.04

0.02
0.04
0.03

Reference
0.00
0.01− 0.01− 0.02− 0.02− 0.04

0.02
0.04
0.03

Depressive
symptoms
before disaster
None
Moderate
SevereMissing

Reference
0.02
0.04− 0.010.00− 0.003− 0.03

0.04
0.07
0.02

Reference
0.02
0.04− 0.01− 0.003− 0.002− 0.04

0.04
0.07
0.01

Missing

0.02
0.05
0.03

0.03
0.06
0.01

Reference

10–12
≥13

Statistical significance at P < 0.05 is indicated in bold. Missing covariates weretreated as the dummy category.
OLS: Ordinary least squares, Coef.: Nonstandardized coefficient, CI: Confidence
interval, IV: Instrumental variable.
a
We excluded 64 participants who lacked information regarding housing
damage.
b
F-statistic in the first stage of IV analysis = 733.0.

Statistical significance at P < 0.05 is indicated in bold. Missing covariates weretreated as the dummy category.
OLS: Ordinary least squares, Coef.: Nonstandardized coefficient, CI: Confidence
interval, IV: Instrumental variable.
a
We excluded 51 participants who lacked information regarding subjective
deterioration of economic circumstances.
b
F-statistic in the first stage of IV analysis = 197.7.

6

T. Ikeda et al.

Social Science & Medicine 264 (2020) 113385

Table 4
Reduction in coefficients by loss of loved ones in disaster, disruption of access to orthopedics after disaster, and psychological distress after disaster (subjective
deterioration of economic circumstances) (n = 2309).
Ordinary least squares regression (without IV)
Model 1aIV regression

Model 2b

Coef.

95% CI

Subjective deterioration of
economic circumstances
Loss of loved ones in disasterLoss
No loss

0.02

0.001

Subjective deterioration of
economic circumstances
Disruption of access toorthopedics after disaster
No
Yes

0.02

Subjective deterioration of
economic circumstances
Psychological distress after
disaster
None
Moderate
Severe0.02

Subjective deterioration of
economic circumstancesc
Change in BMI between baseline
and follow-up (continuous)

0.02

0.001

0.001

− 0.001

0.03

0.03

0.03

0.03

% of
reduction

Coef.

95% CI

0.02

0.00010.03− 0.01− 0.030.01

0.01− 0.002

0.03

0.17

0.05

0.29

0.02

0.0001

0.03

0.01
0.00

− 0.005− 0.03

0.03
0.03

0.02

− 0.001

0.03

0.00

0.00

0.00

− 6.34%− 19.84%− 6.22%− 0.13%Model 1aModel 2b

Coef.

95% CI

0.08

0.03

0.08

0.08

0.07

0.03

0.03

0.03

0.12

0.12

0.12

0.12

% of
reduction

Coef.

95% CI

0.08

0.03

0.12

Reference
0.001− 0.02

0.02

0.07

0.03

0.11

Reference
0.15

0.03

0.27

0.07

0.03

0.12

Reference
0.00− 0.01− 0.02− 0.05

0.02
0.02

0.07

0.03

0.12

− 0.01− 0.01

0.00

0.36%

− 5.33%− 0.15%

0.12%

Statistical significance at P < 0.05 is indicated in bold. Missing covariates were treated as the dummy category.
Coef.: Nonstandardized coefficient, CI: Confidence interval, IV: Instrumental variable.
aAge, sex, educational attainment, pre-disaster income level, osteoporosis/traumatic injuries/fractures before disaster, body mass index (BMI) before disaster,
smoking status before disaster, and depressive symptoms before disaster were adjusted.
b
Potential mediators (loss of loved ones in disaster, disruption of access to orthopedics after disaster, or psychological distress after disaster) were added to Model 1.
c
The number of participants was 2087 as participants whose BMI at baseline and follow-up were missing were excluded.

degree of accuracy, the accessibility of orthopedics services for partici­
pants who needed treatment for joint injuries sustained as a result of thedisaster. Third, we could not identify other mediating factors because oflimited information on this topic. Hence, future studies are expected to
identify other effective prevention strategies for arthritis. Fourth, thegeneralizability of the present results to other natural disasters, such as
hurricanes, is unclear. For example, while hurricanes are somewhat
predictable, earthquakes are not, and the damage from the disaster itself
and its impact on health may be different (Wilson-Genderson et al.,2018). Moreover, it is also unclear whether participants in the present
study are representative of all survivors of the Great East Japan Earth­quake and Tsunami. We analyzed participants in only one municipality,
whereas the influence of the disaster was wide-ranging. For example,
some people suffered from the nuclear power plant accident.

and participated in the interpretation of data and manuscript prepara­tion. All of the authors discussed the data and results and criticallyrevised the manuscript.
Funding
This study used data from the Japan Gerontological Evaluation Study(JAGES). This study was supported by a grant from the National In­
stitutes of Health grants (R01 AG042463 and 2R01 AG042463-06);
several Health and Labour Science Research Grants (Comprehensive
Research on Aging and Health) from the Japanese Ministry of Health,Labour, and Welfare (grants: H22-Choju-Shitei-008, H24-ChojuWakate-009, H25-Choju-Ippan-003, H28-Choju-Ippan-002, and H29Chikyukibo-Ippan-001); Grants-in-Aid for Scientific Research from theJapanese Society for the Promotion of Science (grants: KAKENHI
23243070, 22390400, 24390469, 19K19818, 19H03860, and
20H00557); a grant from the Strategic Research Foundation GrantAided Project for Private Universities from the Japanese Ministry of
Education, Culture, Sports, Science, and Technology (grant: S0991035);
Open Innovation Platform with Enterprises, Research Institute andAcademia (grants: OPERA and JPMJOP1831) from Japan Science andTechnology (JST); the Research Funding for Longevity Sciences from
National Center for Geriatrics and Gerontology (grant: 20–19); andResearch and Development Grants for Longevity Science from the Japan
Agency for Medical Research and Development (grants:
JP19dk0110034, JP20dk0110034). The funders played no role in the
study design, data collection and analysis, or the decision to publish thisreport.

5. Conclusions
We revealed that worsening economic circumstances were causally
related to the new-onset arthritis among survivors of the 2011 Great East
Japan Earthquake and Tsunami. We also showed that the disruption ofaccess to orthopedics was associated with the development of arthritis.
Hence, our study emphasized the importance of recovery and theestablishment of the post-disaster orthopedic medical system in theaftermath of a disaster.
Credit author statement
T.I. created the analysis plan, analyzed the data, and drafted themanuscript. J.A., I.K., K.K., and K.O. supervised data collection for thewhole study. J. A. helped the analyses. I.K. conceived the study design7

T. Ikeda et al.

Social Science & Medicine 264 (2020) 113385

Table 5
Reduction in coefficients by loss of loved ones in disaster, disruption of access to orthopedics after disaster, and psychological distress after disaster (housing damage)(n = 2296).
Ordinary least squares (without IV)
Model 1aIV regression

Model 2b

Coef.

95% CI

Housing damage
Loss of loved ones in disasterLoss
No loss

0.01

0.002

Housing damage
Disruption of access to orthopedics
after disaster
No
Yes

0.01

Housing damage
Psychological distress after
disaster
None
Moderate
Severe0.01

Housing damagec
Change in BMI between baseline
and follow-up (continuous)

0.01

0.002

0.002

0.001

0.02

0.02

0.02

0.02

Coef.

95% CI

0.01

0.001

0.02

− 0.01− 0.03

0.01

0.01

0.001

0.02

0.17

0.06

0.29

0.01

0.001

0.02

0.01
0.01

− 0.004− 0.03

0.03
0.04

0.01
0.00

0.001
− 0.004

0.02
0.01

% of
reduction

Model 1a
Coef.

95% CI− 6.00%

0.02

0.01

− 14.81%− 5.20%− 0.24%

0.02

0.02

0.02

Model 2b

0.01

0.01

0.01

0.04

0.04

0.04

0.04

% of
reduction

Coef.

95% CI

0.02

0.01

0.04

− 0.001− 0.02

0.02

0.02

0.01

0.04

0.17

0.05

0.29

0.02

0.01

0.04

0.01
0.00

− 0.01− 0.03

0.03
0.04

0.02
0.00

0.01
− 0.004

0.04
0.01− 0.58%− 6.07%− 2.62%− 0.08%

Statistical significance at P < 0.05 is indicated in bold. Missing covariates were treated as the dummy category.
Coef.: Nonstandardized coefficient, CI: Confidence interval, IV: Instrumental variable.
aAge, sex, educational attainment, pre-disaster income level, osteoporosis/traumatic injuries/fractures before disaster, body mass index (BMI) before disaster,
smoking status before disaster, and depressive symptoms before disaster were adjusted.
b
Potential mediators (loss of loved ones in disaster, disruption of access to orthopedics after disaster, or psychological distress after disaster) were added to Model 1.
c
The number of participants was 2078 as participants whose BMI at baseline and follow-up were missing were excluded.

Declaration of competing interest

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