Yamada et al. BMC Musculoskeletal Disorders
https://doi.org/10.1186/s12891-022-05694-y(2022) 23:755

Open Access

RESEARCH

Prevalence and municipal variationin chronic musculoskeletal pain
among independent older people: datafrom the Japan Gerontological Evaluation Study(JAGES)
Keiko Yamada1,2* , Tomoko Fujii3 , Yasuhiko Kubota4 , Takaaki Ikeda5,6 , Masamichi Hanazato7 ,Naoki Kondo8 , Ko Matsudaira1,3   and Katsunori Kondo9,10   

Abstract
Background: Urbanization and population aging may affect prevalence of chronic pain from various causes. Thiscross-sectional study aimed to investigate the prevalence of chronic musculoskeletal pain, including some subtypes,
in independent Japanese older people, and whether population density and population aging rate explained prevalence and differences in pain levels between municipalities.
Methods: We analyzed data from 12,883 independent older people living in 58 municipalities who completed
mailed questionnaires and did not need support for daily living. We identified three types of pain: “chronic musculoskeletal pain” lasting ≥ 3 months (overall and in each part of the body), “chronic widespread-type pain” in the spinal
and peripheral area, and “chronic multisite pain” in at least three sites. The latter two were measured using new definitions. These types of pain are correlated with depressive symptoms and we therefore examined the construct validityof the definitions by comparing the Geriatric Depression Scale score. We also used analysis of covariance to compare
the prevalence of these three types of pain between municipalities. Odds ratios, median odds ratios, and the municipal variance in prevalence of chronic musculoskeletal pain were estimated by Bayesian multilevel logistic regression
analysis using the Markov Chain Monte Carlo method.
Results: The construct validity of the definitions of chronic widespread-type pain and chronic multisite pain was
confirmed. The prevalence of the three types of pain (chronic musculoskeletal, widespread, and multisite pain)
was 39.0%, 13.9%, and 10.3%, respectively. Chronic musculoskeletal pain showed a higher prevalence among older
people and women. Individuals in underpopulated, suburban, or metropolitan areas tended to have more pain thanthose in urban areas, but this was not statistically significant (odds ratio [95% credible interval] 1.15 [0.86–1.51], 1.17
[0.93–1.43], 1.17 [0.94–1.46]). Population density and population aging rate did not explain the differences between
municipalities.

*Correspondence: keiko-yamada@umin.org
1
Department of Anesthesiology and Pain Medicine, Juntendo University
Faculty of Medicine, Tokyo, Japan
Full list of author information is available at the end of the article© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Yamada et al. BMC Musculoskeletal Disorders(2022) 23:755

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Conclusions: The prevalence of chronic musculoskeletal pain was consistent with previous global reports. Areas with
overpopulation and depopulation tended to have higher pain prevalence, but population density and population
aging rate did not explain municipal variance. Further research is needed to identify other factors that contribute toregional variance.
Keywords: Aged, Chronic pain, Cohort studies, Musculoskeletal pain, Widespread chronic pain

Background
Population aging may affect the prevalence of chronic
pain from various causes associated with injury or illness[1]. A systematic review reported that global prevalenceof chronic pain among community-dwelling older people was 25%–76% [1]. However, Japanese data were notincluded in that review because no Japanese studies met
the inclusion criteria. A study in Japan limited to 6,000
residents over 20 years old in a single city found that theprevalence of chronic pain was 61.6% among people aged
60–69, 72.1% for those in their 70 s, 56.4% for those in
their 80 s, and 32.5% for those in their 90 s [2]. Anotherstudy among 13,217 residents over 40 years old in several municipalities was limited to anatomically localized
pain. It found a prevalence of 20.9% for chronic lower
back pain and 18.3% for chronic knee pain [3]. However,no large epidemiological studies of chronic pain havefocused on community-dwelling older Japanese people.
Urbanization may also affect the prevalence and severity of chronic pain. A previous study in North Dakota
reported that rural residents (i.e., those living in areas
with a population of less than 2,500 people) showed ahigher prevalence of chronic pain than urban residents[4]. Another study in North Carolina reported thatrural residents with chronic low back pain had greater
functional limitation and poorer function than urban
residents with chronic low back pain [5]. However, thestudy included no definition of rural. For musculoskeletal concerns related to chronic pain, a systematic review
concluded that hip fractures were more likely to be experienced by urban than rural residents [6]. However, nodemographic studies of chronic pain have focused onJapanese residents.
The criteria used to define chronic widespread pain
and chronic multisite pain vary, but the underlying clinical concepts are quite similar. They are included in the
category of chronic primary pain in the 11th revision of
the International Classification of Diseases (ICD-11) for
2022 [7–10]. A systematic review concluded that prevalence of chronic widespread pain in the general population was 10%–15%, and that it was higher in women andthose over 40 years old [10]. Chronic widespread pain is
a cardinal symptom of fibromyalgia, but it is also commonly observed in patients with several other diseases[7, 9, 11]. Previous research suggested that both chronic

multisite pain and multisite pain among older people
were associated with reductions in physical function [12,13], psychological distress (e.g., anxiety and depressive
symptoms) and socioeconomic factors [12, 13]. Chronic
widespread pain and chronic multisite pain are important concepts in geriatric pain medicine, but there is little evidence about their prevalence among older Japanesepeople.
There is regional variation in healthy life expectancy inJapan [14]. However, no studies have explored regionalvariation in prevalence of chronic musculoskeletal pain.
We hypothesized that regional variation would exist, andmight be partly explained by urbanization and population aging because lifestyles vary significantly between
metropolitan and rural areas.
This study used data from a large cohort study, the2019 Japan Gerontological Evaluation Study (JAGES)[15], across 58 municipalities. We aimed to investigate
the prevalence of chronic pain (overall chronic musculoskeletal pain, pain in each musculoskeletal part of thebody, chronic widespread pain, and chronic multisitepain) in independent older Japanese people. We also
investigated whether population density and population
aging rate were associated with prevalence and municipal
variation in chronic musculoskeletal pain using Bayesian
multilevel regression analysis.

Methods
Study design and study population

This study was cross-sectional in design. In JAGES 2019,self-administered questionnaires were mailed to older
residents (aged ≥ 65 years) identified from 2019 official
residential registers of Japanese local governments. Residents who wished to participate completed and returned
the questionnaire (response rate: 69.4%).
In total, 24,342 participants who did not receive benefits from the national long-term care insurance completed the questionnaires, including items on theirexperience of chronic musculoskeletal pain. Supplementary Fig. 1 shows the enrollment process. We excluded
2,879 participants who needed support for daily living
or were missing data on activities of daily living. We also
excluded 10 participants missing data on municipality of residence, 6,049 participants missing data for pain
items, 1,876 participants missing data for the GeriatricYamada et al. BMC Musculoskeletal Disorders(2022) 23:755

Depression Scale items, and 617 participants with historyof cancer. We also excluded seven participants from one
municipality because we considered that data from fewer
than 10 residents would be insufficient for multilevel
analysis. An additional 21 participants were excluded
because they lived in a small village near Fukushima
nuclear power plant, and we considered that the effectsof evacuation might be potential confounders of theinfluence of population density and aging rate on chronic
pain. This left a total of 12,883 community-dwelling older
people (6,687 men and 6,196 women) living in 58 municipalities, and not in need of support for daily living. The
58 municipalities were dispersed across Japan, as shownin Fig. 1.
Measures
Chronic musculoskeletal pain

We collected data about experience of pain in designated musculoskeletal sites (neck, shoulder, elbow, wrist,
finger, back, lower back, hip, knee, ankle, and toe) lasting ≥ 3 months in the past year.
The 2019 JAGES questionnaire enabled us to identify
lower back pain and knee pain separately from pain inother musculoskeletal parts of the body, using independent questions with a figure. The question for lower

Page 3 of 12

back pain was, “Did you have lower back pain (pain in
the area shown in the figure) persisting a day or morein the past year? (yes or no)”. Those who answered yes
were asked, “How long did you have lower back pain?
(< 1 month, ≥ 1 month and < 3 months, or ≥ 3 months)”.
We defined respondents with lower back pain lasting for ≥ 3 months in the past year as having chronic
lower back pain. Similarly, the question for knee pain
was, “Did you have knee pain (pain at the site shownin the figure) persisting a day or more in the past year?
(yes or no)”. Those who answered yes were asked, “Howlong did you have knee pain? (< 1 month, ≥ 1 monthand < 3 months, or ≥ 3 months)”. We defined respondents with knee pain lasting for ≥ 3 months in the past
year as having chronic knee pain. For chronic pain inother musculoskeletal parts of the body, we used thesingle question, “Did you have any pain in other partsof the body lasting 3 months or more in the past year
(multiple answers: none, neck, shoulder, elbow, wrist,
finger, back, hip, ankle, and toe)?”.
The forthcoming ICD-11 defines chronic pain as persistent or recurrent pain lasting at least 3 months [8].
We therefore defined pain lasting ≥ 3 months in at least
one of these areas as “chronic musculoskeletal pain”.

Fig. 1 The 58 Japanese municipalities enrolled in this study. The study municipalities are shown in red on the mapYamada et al. BMC Musculoskeletal Disorders(2022) 23:755

Chronic widespread‑type pain and chronic multisite pain

There is no single agreed definition for chronic widespread pain [7]. We therefore created a new definitionof “chronic widespread-type pain” as pain lasting ≥ 3months in both the spinal area (i.e., neck, back, or lowerback) and any peripheral area (i.e., shoulder, elbow, wrist,
finger, hip, knee, ankle, and toe). This definition drew
on two criteria for chronic widespread pain: the American College of Rheumatology (ACR) and the Manchester criteria. The ACR criteria suggest that widespread
pain occurs in all body quadrants (on both sides of the
body and above/below the waist; ‘side’ is considered toinclude right or left shoulder and buttock, so limb pain is
not necessary), and also axially (cervical spine or anterior
chest or thoracic spine or lumbar spine)[16]. The Manchester criteria define widespread pain as pain in at least
two sections of two contralateral limbs (i.e., left arm/rightleg or right arm/left leg) and axial skeletal pain (including
the lower back) [17, 18].
The term “multisite pain”, or pain occurring simultaneously at multiple anatomical sites, is also used academically and clinically. Previous epidemiological studies
found that classification of multiple anatomical sites
could be based on the number of pain sites [19, 20]. We
used the ACR suggestion that “pain in three sites (e.g.,right shoulder, left buttock, and thoracic spine) qualifies as widespread pain” [16], and therefore created a
new definition of “chronic multisite pain” as pain lasting ≥ 3 months in three or more anatomical sites.
Geriatric Depression Scale

To confirm the construct validity of our new definitionsof chronic widespread-type pain and chronic multisite
pain, we used the Geriatric Depression Scale (GDS). Previous studies compared widespread pain to depressive
symptoms measured using the General Health Questionnaire [18, 21]. However, we selected the GDS because it
was considered best for assessing depressive symptoms
among older people in the general population. The Japanese version of GDS is used to assess the depressive
symptoms of older people [22]. Its validity and reliability
have previously been confirmed [22].
Demographic data

The questionnaire included questions about municipality of residence, age, gender, educational attainment(< 6 years, 6–9 years, 10–12 years, ≥ 13 years, and other),and marital status (married, widowed, divorced, single,
and other).
Information about population size and municipality
inhabitable area (ha) was taken from social and demographic statistics from 2018 [23], and national census
data from 2019 [24]. Population density (people/km2) was

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obtained by dividing the overall population by municipal
area (ha converted into ­km2). Population aging rate (%)
was obtained by dividing the population aged 65 years
and over by the overall population.
We used population density as a categorical variable forinterpretability. We defined areas with a population density < 200 people/km2 as underpopulated, 200–1,999 people/km2 as suburban, 2000–3,999 people/km2 as urban,
and ≥ 4000 people/km2 as metropolitan. These definitions were adapted from criteria used in analysis of designated zones for medical administration in Japan and thedefinition of densely inhabited districts (DID) used in theJapanese census [25, 26].
Health‑related measures

The questionnaires asked about history of hypertension,
diabetes mellitus, and hyperlipidemia (yes or no).
Analytical procedure

Prevalence of each type of chronic pain was calculated byage group (65–69, 70–74, 75–79, 80–84, and ≥ 85 years
old) and gender (men and women). We examined differences in depressive symptoms assessed using GDS
by pain location, in line with previous studies using theGeneral Health Questionnaire [15]. Age- and genderadjusted means of GDS scores for each chronic pain
distribution (peripheral area only, spinal area only, orchronic widespread-type pain [peripheral + spinal area])
and number of anatomical pain sites (1, 2, or ≥ 3, ranging
from 1 to 11) were compared to no pain using analysis ofcovariance with Dunnett’s test. P for trend in GDS scoresby number of anatomical pain sites was calculated using
a general linear model. Municipal differences in age- andgender-adjusted prevalence for each type of chronic musculoskeletal pain were tested using analysis of covariancewith Tukey’s test.
We investigated associations between age (5-yearincrements), gender, population density (< 200, 200–
1,999, 2000–3,999, and ≥ 4000), and population aging
rate (tertiles) and the prevalence and municipal variationin overall chronic musculoskeletal pain for all chronic
pain types. Supplementary Fig. 2 shows the distributionof these factors among municipalities. Odds ratios (ORs)with 95% credible intervals (CI), median ORs, and therandom parameters accounting for municipal variance inpain prevalence were estimated using Bayesian multilevel
logistic regression analysis and the Markov Chain Monte
Carlo (MCMC) method with 10,000 iterations. ORs wereconsidered significant if the 95% CI did not include one.
Model 1 included age (5-year increments) and gender.
Model 2 added population density (< 200, 200–1,999,
2000–3,999, and ≥ 4000 person/km2). Model 3 added
population aging rate (tertiles, %).

Yamada et al. BMC Musculoskeletal Disorders(2022) 23:755

To determine the sample size for a multilevel logistic
regression model, a previous simulation study concluded
that an unbiased fixed effect parameters estimate wasachieved by having ≥ 50 groups, each of at least 50 subjects, under maximum likelihood methods [27]. We haddata from 58 (> 50) municipalities (i.e., groups). Of these58 municipalities, 54 included > 50 residents (i.e., groupsize), and the remaining four municipalities included > 30
residents. As a sensitivity analysis, we also reran theBayesian multilevel logistic regression analysis using thedataset that eliminated the four municipalities with fewer
than 50 residents.
Bayesian multilevel logistic regression analysis used
Stata Statistical Software: Release 16 (StataCorp LLC,
2019. College Station, TX, USA). Other statistical analyses used SAS, Version 9.4 (SAS Institute Inc., Cary, NC,
USA).

Results
Table 1 shows the characteristics of participants. Meanage was 73.6 (standard deviation 6.0) years old, and thestudy included approximately the same number of men(51.9%) and women (48.1%). The proportion of participants with < 10 years educational attainment was 19.6%,
and 75.5% of participants were married. Population aging
rate was ≥ 21.0% in all municipalities.
Table 2 shows the prevalence of each type of pain byage group and gender (ordering: chronic musculoskeletal pain, rank order of the prevalence of each pain site,
chronic widespread-type pain, and chronic multisitepain). The prevalence of chronic musculoskeletal pain
was 39.0% (men 36.3% and women 41.8%), and increased
with age. The top three pain sites by prevalence wereshoulder (14.6%), lower back (13.6%), and knee (11.8%).
Supplementary Table 1 shows ranking of prevalence of
pain site.
The prevalence of chronic musculoskeletal pain by category of population density was 39.1% for underpopulated, 39.6% for suburban, 35.9% for urban, and 38.8%
for metropolitan areas. For chronic multisite-type pain,
the corresponding figures were 14.8%, 14.3%, 13.7%, and13.6%, and for chronic multisite pain we found 11.6%,
10.3%, 9.1%, and 10.3% (not shown in the table).
Table 3 shows adjusted mean GDS scores by pain distribution and number of pain sites. Participants with pain
in only peripheral areas or spinal areas had higher GDS
scores than those without pain. Participants with chronic
widespread-type pain had the highest GDS scores. GDS
score increased with number of pain sites (0, 1, 2, ≥ 3)
in a dose–response manner (p for trend < 0.001). Supplementary Table 2 shows the full results for anatomical
pain sites.

Page 5 of 12

Table 1 Mean values and proportions for demographic factors(n = 12,883)
Number of municipalities

n

%

58

-

Age, years
65–69

3721

28.9

70–74

4004

31.1

75–79

2922

22.7

80–84

1565

12.1

  
≥ 85

671

5.2

Gender
Men

6687

51.9

Women

6196

48.1

Educational attainment
Under 6 years

40

0.3

6–9 years

2481

19.3

10–12 years

5696

44.2

13 years and more

4477

34.8

Other

78

0.6

Missing111

0.9

Marital status
Married

9731

75.5

Widowed

2007

15.6

Divorced593

4.6

Single

413

3.2

Other

53

0.4

Missing86

0.7

History of hypertension

5535

43.0

History of diabetes

1667

12.9

History of hyperlipidemia

2009

15.6

GDS scores: 0–15
Population density, people/km2 (municipality level)< 200: underpopulated area

Mean

SD

2.8

2.9

n

%

650

5.0

200–1,999: suburban area

5,868

45.5

2000–3,999: urban area

970

7.5

≥ 4000: metropolitan area

5,395

41.9

Population aging rate, % (municipality level)
Minimum-

21.0

Median-25.7

Maximum-

45.7

First tertile: 21.0 to 24.3

3,864

30.0

Second tertile: 24.7 to 27.8

4,939

38.3

Third tertile: 28.4 to 45.7

4,080

31.7

GDS Geriatric Depression Scale, SD Standard deviation

Chronic musculoskeletal pain prevalence by municipality is shown in Fig. 2. This varied significantly acrossmunicipalities (p < 0.001), ranging from 28.2% to 53.3%,
with a median of 38.8%. Supplementary Table 3 and Supplementary Fig. 3 show the prevalence of pain in specificYamada et al. BMC Musculoskeletal Disorders(2022) 23:755

Page 6 of 12

Table 2 Prevalence of chronic pain by age group and gender (n = 12,883)
Participant group

All

CMP

ShoulderLower back

Knee

Neck

FingerHip

Age group (years)

Total

Men

Women

n

%

n

%

n

%

65–69

3721

100

1926

100

1795

100

70–74

4004

100

2064

100

1940

100

75–79

2922

100

1489

100

1433

100

80–84

1565

100

842

100

723

100

≥ 85

671

100

366

100

305

100

Total

12,883

100

6687

100

6196

100

65–69

1402

37.7

683

35.5

719

40.1

70–74

1469

36.7

711

34.4

758

39.1

75–79

1210

41.4

560

37.6

650

45.4

80–84

655

41.9

334

39.7

321

44.4

≥ 85

284

42.3

140

38.3

144

47.2

Total

5020

39.0

2428

36.3

2592

41.8

65–69

549

14.8

274

14.2

275

15.3

70–74

582

14.5

290

14.1

292

15.1

75–79

427

14.6

192

12.9

235

16.4

80–84

240

15.3

120

14.3

120

16.6

≥ 85

88

13.1

40

10.9

48

15.7

Total

1886

14.6

916

13.7

970

15.7

65–69

432

11.6

233

12.1

199

11.1

70–74

473

11.8

251

12.2

222

11.4

75–79

465

15.9

227

15.2

238

16.6

80–84

261

16.7

137

16.3

124

17.2

≥ 85

126

18.8

64

17.5

62

20.3

Total

1757

13.6

912

13.6

845

13.6

65–69

367

9.9

149

7.7

218

12.1

70–74

409

10.2

148

7.2

261

13.5

75–79

380

13.0

134

9.0

246

17.2

80–84

244

15.6

113

13.4

131

18.1

≥ 85

126

18.8

49

13.4

77

25.2

Total

1526

11.8

593

8.9

933

15.1

65–69

237

6.4

136

7.1

101

5.6

70–74

271

6.8

134

6.5

137

7.1

75–79

205

7.0

109

7.3

96

6.7

80–84

113

7.2

60

7.1

53

7.3

≥ 85

37

5.5

20

5.5

17

5.6

Total

863

6.7

459

6.9

404

6.5

65–69

261

7.0

101

5.2

160

8.9

70–74

260

6.5

99

4.8

161

8.3

75–79

197

6.7

86

5.8

111

7.7

80–84

97

6.2

39

4.6

58

8.0

≥ 85

32

4.8

11

3.0

21

6.9

Total

847

6.6

336

5.0

511

8.2

65–69

213

5.7

95

4.9

118

6.6

70–74

232

5.8

115

5.6

117

6.0

75–79

206

7.0

91

6.1

115

8.0

80–84

128

8.2

74

8.8

54

7.5

≥ 85

56

8.3

29

7.9

27

8.9

835

6.5

404

6.0

431

7.0

Total

Yamada et al. BMC Musculoskeletal Disorders(2022) 23:755

Page 7 of 12

Table 2 (continued)
Participant group

Back

Ankle

Wrist

Toe

Elbow

CWTP

CMSP

Age group (years)

Total

Men

Women

n

%

n

%

n

%

65–69

158

4.2

73

3.8

85

4.7

70–74

184

4.6

90

4.4

94

4.8

75–79

156

5.3

66

4.4

90

6.3

80–84

99

6.3

48

5.7

51

7.1

≥ 85

34

5.1

18

4.9

16

5.2

Total

631

4.9

295

4.4

336

5.4

65–69

117

3.1

44

2.3

73

4.1

70–74

120

3.0

50

2.4

70

3.6

75–79

125

4.3

48

3.2

77

5.4

80–84

86

5.5

36

4.3

50

6.9

≥ 85

32

4.8

18

4.9

14

4.6

Total

480

3.7

196

2.9

284

4.6

65–69

138

3.7

53

2.8

85

4.7

70–74

118

2.9

47

2.3

71

3.7

75–79

92

3.1

35

2.4

57

4.0

80–84

52

3.3

26

3.1

26

3.6

≥ 85

34

5.1

11

3.0

23

7.5

Total

434

3.4

172

2.6

262

4.2

65–69

99

2.7

39

2.0

60

3.3

70–74

105

2.6

42

2.0

63

3.2

75–79

102

3.5

38

2.6

64

4.5

80–84

75

4.8

36

4.3

39

5.4

≥ 85

23

3.4

13

3.6

10

3.3

Total

404

3.1

168

2.5

236

3.8

65–69

112

3.0

69

3.6

43

2.4

70–74

98

2.4

59

2.9

39

2.0

75–79

91

3.1

53

3.6

38

2.7

80–84

52

3.3

35

4.2

17

2.4

≥ 85

24

3.6

11

3.0

13

4.3

Total

377

2.9

227

3.4

150

2.4

65–69

455

12.2

218

11.3

237

13.2

70–74

492

12.3

231

11.2

261

13.5

75–79

469

16.1

197

13.2

272

19.0

80–84

262

16.7

129

15.3

133

18.4

≥ 85

119

17.7

55

15.0

64

21.0

Total

1797

13.9

830

12.4

967

15.6

65–69

338

9.1

154

8.0

184

10.3

70–74

360

9.0

160

7.8

200

10.3

75–79

328

11.2

131

8.8

197

13.7

80–84

206

13.2

98

11.6

108

14.9

≥ 85

89

13.3

38

10.4

51

16.7

1321

10.3

581

8.7

740

11.9

Total

CMP Chronic musculoskeletal pain, CWTP Chronic widespread-type pain, CMSP Chronic multisite pain
Chronic widespread-type pain was defined as chronic pain in both the spinal area (i.e., neck, back, or lower back) and any peripheral area
Chronic multisite pain was defined as chronic pain in three or more sites
Proportions were adjusted for age and genderYamada et al. BMC Musculoskeletal Disorders(2022) 23:755

Page 8 of 12

Table 3 Pain location and Geriatric Depression Scale (GDS) scores (n = 12,883)
Number(%)

GDS score
Adjusted meanSE

P value(reference)

Pain distribution
No pain

7863

61.0

2.4

0.03

Peripheral area only

2483

19.3

3.2

0.06

< .0001

Spinal area only

740

5.7

3.3

0.10

< .0001

CWTP (Peripheral + spinal area)

1797

13.9

4.0

0.07

< 0.001(reference)

Number of pain sites
0

7863

61

2.4

0.03

1

2390

18.6

3.1

0.06

< 0.001

2

1309

10.2

3.5

0.08

< 0.001≥ 3 (CMSP)

1321

10.3

4.3

0.08

< 0.001

P for trend < 0.001

GDS Geriatric depression scale, SE Standard error, CWTP Chronic widespread-type pain, CMSP: chronic multisite pain. Means of GDS scores were adjusted for age andgender. P value was tested using analysis of covariance. P for linear trend was calculated using a general linear model
P for linear trend < 0.001

Fig. 2 Prevalence of overall chronic musculoskeletal pain by municipality. The X-axis shows municipality IDs, and the Y-axis shows the prevalence ofchronic musculoskeletal pain (%). The maximum, median, and minimum prevalence were 53.3%, 38.8%, 28.2%

anatomical pain sites, by population, inhabitable area,and population density of municipality. Significant differences were found between municipalities for pain inneck (p = 0.01), shoulder (p = 0.02), knee (p = 0.02), andtoe (p = 0.01).
Table 4 shows ORs (95% CI) and median ORs of prevalence of chronic musculoskeletal pain with betweenmunicipality variance. There was a higher prevalence inolder people and women. Aging and gender contributed
3.4% to between-municipality variance. People living
in underpopulated, suburban, and metropolitan areas
were more likely to have chronic musculoskeletal pain
than those in urban areas, although this was not statistically significant. Median ORs for underpopulated, suburban, and metropolitan areas were 1.15, 1.16, and 1.16.
However, population density did not explain betweenmunicipality variance in chronic musculoskeletal pain.
Population aging rate did not explain either prevalence or

between-municipality variance in chronic musculoskeletal pain. The results of the sensitivity analysis for the ORs(95% CI) and median ORs of prevalence of chronic musculoskeletal pain with between-municipality variance,
using the dataset eliminating the four municipalities withfewer than 50 residents, were similar to these results.

Discussion
The population aging rate in all 58 municipalities studied
was ≥ 21.0%, the World Health Organization and United
Nations definition of a super-aged society [28]. We founda prevalence of 39.0% (men 36.3%, women 41.8%) for
chronic musculoskeletal pain, 13.9% (men 12.4%, women
15.6%) for chronic widespread-type pain, and 10.3% (men8.7%, women 11.9%) for chronic multisite pain. The top
three anatomical sites by prevalence were shoulder, lower
back, and knee. Individuals with chronic widespreadtype or chronic multisite pain were more likely than0.0170 (0.0005)

1.13

1.06

1.13

1.13

+ 3.4% (vs. Null model)

1.14− 10.0% (vs. Model 1)− 1.1% (vs. Model 2)
1.140.0189 (0.0007)

Model 3: Adjusted for age, gender, population density, and population aging rate

Model 2: Adjusted for age, gender, and population density

Model 1: Adjusted for age and gender

MOR

0.87–1.21

0.89–1.14

0.91–1.27

0.91–1.25

0.79–1.35

1.03

1.02

1.16

1.05

1.05

1.18–1.36 1.27

1.04–1.11 1.07

95% CICI Credible interval, CMP Chronic musculoskeletal pain, MCSE Monte Carlo standard error, MOR Median odds ratio, OR Odds ratio. Bayesian multilevel logistic regression model was used to estimate the randomparameters accounting for variation between municipalities. Statistically significant results are shown in boldMedian odds ratio between municipalities

Proportional changes variance

Between-municipality variance

1.02
1.04

Third tertile

1.07

1 (reference)

1.06

1 (reference)

1.16

1.16

1.27

1.07

OR

Second tertile

0.94–1.46

0.93–1.43

0.86–1.51

1.18–1.35 1.27

1.04–1.11 1.08

MOR

Model 3: Added population aging rate

First tertile

0.0187 (0.0005)

1.17

Population aging rate

1 (reference)

2000–3,999: urban area

≥ 4000: metropolitan area

1.15

1.27

1.07

1.17

1.27

1.07

< 200: underpopulated area

1.18–1.36

1.04–1.11

200–1,999: suburban area

0.0176 (0.0004)

1.27

Population density, people/km2

1.07

Women (ref. men)

95% CI

OR

MOR

OR

95% CI

Model 2: Added population density

Model 1: Individual variables

Age, 5 year increments

Null model:

Table 4 Odds ratios (95% credible intervals) and median odds ratios of prevalence of chronic pain: the results of Bayesian multilevel logistic regression analysis

Yamada et al. BMC Musculoskeletal Disorders(2022) 23:755
Page 9 of 12

Yamada et al. BMC Musculoskeletal Disorders(2022) 23:755

individuals without pain to show high levels of depressive
symptoms. Older people and women had a higher prevalence of chronic musculoskeletal pain. People living inunderpopulated, suburban, or metropolitan areas tended
to have more chronic musculoskeletal pain than those inurban areas, but this was not statistically significant. Population density and population aging rate were not associated with differences between municipalities.
A previous study in a single Japanese city foundchronic pain prevalence of over 50% among those in their
60 s–80 s [2], which was higher than our figure of 39% forchronic musculoskeletal pain. This may be because thatstudy included chronic pain other than musculoskeletal
pain, such as headaches and abdominal pain.
Residential environmental factors related to population density that may affect chronic musculoskeletal pain
prevalence include city design, geographical situation,and climate. For example, many Japanese provinces are
hilly. A previous study found neighborhood walkability
was associated with knee pain in older people [29]. There
may also be differences in medical care between urban
and underpopulated areas that may affect chronic musculoskeletal pain prevalence.
To our knowledge, this is the first study to report onprevalence of chronic widespread-type or chronic multisite pain in independent older Japanese people. Thereis no consistent definition of chronic widespread painor chronic multisite pain. We therefore defined chronic
widespread-type pain as pain experienced in both thespinal area and at least one peripheral area, and chronic
multisite pain as pain experienced in ≥ three anatomicalsites, drawing on several previous criteria. Individuals
meeting our definition of chronic widespread pain andchronic multisite pain were more likely to have depressive symptoms than those without pain. This result is
consistent with previous studies [18, 21], suggesting thatour definitions were valid.
Population density was not associated with municipal
variation in chronic musculoskeletal pain prevalence.
Further research is needed to explore additional factorsthat contribute to municipal variation in chronic musculoskeletal pain prevalence, such as socioeconomic factors[30].
Chronic pain has negative effects on ability to perform
activities of daily living and may increase the burden
on caregivers [31]. As the number of older people withchronic pain increases, so will the social burden. Policymakers and healthcare providers should consider developing a strategy on chronic pain as we move toward asuper-aging society.
The strength of our study was using data from a large
cohort study coordinated with local governments. However, this study also had several limitations. First, wePage 10 of 12

examined only 58 of 1,718 municipalities in Japan, andthe results may therefore not be fully representative.
However, the mean rate of population aging in our study(27.8%) was similar to the rate of 28.4% reported for Japanas a whole in 2019 [24]. Second, the dataset for JAGES
excluded residents who received national long-term careinsurance benefits. We therefore excluded participants
who needed support for daily living even if they did notreceive benefits. The major reasons for needing longterm care insurance benefits among older Japanese residents are dementia and musculoskeletal disorders [32].
It is difficult for older people with dementia to respondaccurately to self-reported questionnaires. We therefore
excluded anyone receiving long-term care benefits orneeding support for daily living. However, this population has a higher level of comorbidities, including musculoskeletal disorders related to disability and pain. Older
people with disabilities that resulted from severe chronic
musculoskeletal pain could therefore have been excluded
from the analyses, so our results may underestimate
chronic musculoskeletal pain prevalence in community-dwelling people. An external framework should beestablished in the future to investigate older people who
received benefits from the national long-term care insurance or were in long-care facilities and hospitals. Third,
we did not have information about treatment or medication for pain. Prescribed or over-the-counter analgesics
may suppress pain symptoms, which could have influenced the results. Finally, we did not collect data aboutchronic pain other than musculoskeletal pain. Headache,orofacial pain, and visceral pain are also important healthproblems among older people. However, chronic musculoskeletal pain is the dominant type of chronic pain for allages, and directly influences physical mobility. We therefore believe it is important to understand chronic musculoskeletal pain prevalence among independent older
people to inform health policy.

Conclusions
Chronic musculoskeletal pain prevalence in independent
older Japanese people in this study was consistent withprevious global reports. Population density tended to
be associated with chronic musculoskeletal pain prevalence, although this was not statistically significant, anddid not explain municipal variation. Population aging
rate was also not associated with chronic musculoskeletal pain prevalence and municipal variation. Further
research is needed to investigate other factors that contribute to regional variance. Our findings may inform
healthcare policy for chronic pain in older people, andadd to the evidence about environmental factors affecting chronic pain.

Yamada et al. BMC Musculoskeletal Disorders(2022) 23:755

Abbreviations
ICD-11: 11th revision of the International Classification of Diseases; JAGES:
Japan Gerontological Evaluation Study; ACR​: American College of Rheumatology; GDS: Geriatric Depression Scale; OR: Odds ratio; CI: Credible intervals;
MCMC: Markov Chain Monte Carlo.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12891-​022-​05694-y.
Additional file 1: Figure S1. Enrollment process for participants.
Table S1. Rankings of pain sites. Table S2. Number of pain sites and Geriatric Depression Scale (GDS) scores (n = 12,883). Table S3. Demographic
factors and prevalence of chronic pain among participants by municipality (n = 58). Figure S2. Distribution of age, gender, population density,and aging rate. The Xaxis indicates municipality IDs. The Y-axes indicate
means of age (year), proportion of women (%), population density(people/km2 ), and aging rate (%). Figure S3. Prevalence of each type ofchronic pain. The X-axis indicates the prevalence of each type of chronic
musculoskeletal pain (CMP) (%), and the Y-axis indicates municipality
IDs. The maximum, median, and minimum prevalence of CMP are also
indicated.
Acknowledgements
The authors are grateful to all the participants in this study and all individuals involved
in data collection. The authors also thank Dr. Toshiyuki Ojima, Hamamatsu University
School of Medicine for his technical supports. We thank Melissa Leffler, MBA, from
Edanz Group (https://​jp.​edanz.​com/​ac) for editing a draft of this manuscript.
Authors’ contributions
KY: study design, analysis and interpretation of data, drafting the article; TF:
interpretation of data, and revising the manuscript critically for important
intellectual content; YK: interpretation of data, and revising the manuscript
critically for important intellectual content; TI: interpretation of data, andrevising the manuscript critically for important intellectual content; MH:
acquisition of data, interpretation of data and revising the manuscript criticallyfor important intellectual content; NK: acquisition of data, interpretation ofdata, and revising the manuscript critically for important intellectual content;
KM: interpretation of data, and revising the manuscript critically for important
intellectual content; KK: acquisition of data, interpretation of data, and revising the manuscript critically for important intellectual content. All authors
approved the final version and agreed to be accountable for all aspects of thework, ensuring that questions related to the accuracy or integrity of any partof the work are appropriately investigated and resolved.
Funding
This study used data from JAGES (the Japan Gerontological Evaluation Study),
which was supported by JSPS (Japan Society for the Promotion of Science)
KAKENHI Grant Number (JP15H01972, 18H00953, 18H03047, 18H04071,
19K19818, 19H03860, 19H03915, 20H00557, 20H03954, 21H03196, 21K11108),
Health Labour Sciences Research Grant (H28-Choju-Ippan-002, 19FA2001,
19FA1012, 21GA0101), Japan Agency for Medical Research and Development(AMED) (JP17dk0110017, JP18dk0110027, JP18ls0110002, JP18le0110009,
JP19dk0110034, JP20dk0110034, JP21dk0110037), Open Innovation Platform
with Enterprises, Research Institute and Academia (OPERA, JPMJOP1831) fromthe Japan Science and Technology (JST), a grant from Innovative Research
Program on Suicide Countermeasures (1–4), a grant from Sasakawa Sports
Foundation, a grant from Japan Health Promotion & Fitness Foundation, a
grant from Chiba Foundation for Health Promotion & Disease Prevention, the8020 Research Grant for fiscal year 2019 from the 8020 Promotion Foundation(adopted number: 19–2-06), a grant from Niimi University (1915010), grants
from Meiji Yasuda Life Foundation of Health and Welfare and the Research
Funding for Longevity Sciences from National Center for Geriatrics and Gerontology (20–19, 21–20, 29–42, 30–22). This study was also funded by a Health
Labour Sciences Research Grant [grant number 19FG2001, 22FG2001]. This
research was partially supported by JSPS Overseas Research Fellowships (toK.Y.). The findings and conclusions of this article are the sole responsibility of
the authors and do not represent the official views of the research funders.

Page 11 of 12

Availability of data and materials
All enquiries should be addressed to the data management committee via e-mail:dataadmin.ml@jages.net. All JAGES datasets have ethical or legal restrictions for
public deposition owing to inclusion of sensitive information from the participants.
Following the regulation of the local governments that cooperated with our survey,
the JAGES data management committee has imposed restrictions upon the data.

Declarations
Ethics approval and consent to participate
All procedures were in accordance with the ethical standards of the Helsinki
Declaration of 1975, revised in 2013. The study protocol of JAGES was reviewed
and approved by the Ethics Committee at Chiba University Faculty of Medicine(approval number 331 and 1201), and the Ethics and Conflict of Interest
Management Committee, Center for Gerontology and Social Science, National
Center for Geriatrics and Gerontology (approval number 992–2 and 1274–2),
and the Ethics Committee at the Japan Agency for Gerontological Evaluation
Study (approval number 2019–01). The protocol for this study was also reviewed
and approved by the Ethics Committee at Juntendo University Faculty of
Medicine (approval number 2021032). All participants provided their written
informed consent before they responded and returned the questionnaire by
mail in this study. For any participants without written declaration of intention,
written informed consent was assumed with the voluntary return of the questionnaire. The ethics committees approved this use of assumed consent.
Consent for publication
Not applicable.
Competing interests
KM has declared the following potential conflicts of interest. KM is ashareholder/ adviser of Trunk Solution Co., Ltd., and received the following
support: grant support from Sompo Holdings, Inc, MS&AD InterRisk Research& Consulting, Inc., Inotech Co., Ltd., NUVASIVE Japan MTG Co., Ltd., DeNA,
Medical Data Scientist and Medical AI Device Development Organization,
The Association for Preventive Medicine of Japan, Murata Manufacturing Co.,
Ltd. including personal fees from Nippon Zoki Pharmaceutical Co., Ltd., Ono
Pharmaceutical Co. Ltd., Chugai Pharmaceutical Co., Ltd., AYUMI Pharmaceutical Corporation, Shionogi & Co., Ltd.; lecture fees from Eli Lilly Japan K.K, Pfizer
Japan Inc., Hisamitsu Pharmaceutical Co., Inc., from outside the submitted
work. KY, TF, MH, YK, TI, NK and KK have no competing interests to declare.
Author details
1
Department of Anesthesiology and Pain Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan. 2 Pain Medicine, Juntendo University Graduate
School of Medicine, Tokyo, Japan. 3 Department of Medical Research and Management for Musculoskeletal Pain, 22nd Century Medical and Research Center,the University of Tokyo Hospital, Tokyo, Japan. 4 Osaka Center for Cancer
and Cardiovascular Diseases Prevention, Osaka, Japan. 5 Department of Health
Policy Science, Graduate School of Medical Science, Yamagata University,
Yamagata, Japan. 6 Department of International and Community Oral Health,
Tohoku University Graduate School of Dentistry, Sendai, Japan. 7 Departmentof Environmental Preventive Medical Sciences, Center for Preventive Medical
Sciences, Chiba University, Chiba, Japan. 8 Department of Social Epidemiology,
Graduate School of Medicine and School of Public Health, Kyoto University,
Kyoto, Japan. 9 Department of Social Preventive Medical Sciences, Center
for Preventive Medical Sciences, Chiba University, Chiba, Japan. 10 Departmentof Gerontological Evaluation, Center for Gerontology and Social Science,
National Center for Geriatrics and Gerontology, Aichi, Japan.
Received: 6 February 2022 Accepted: 22 July 2022

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