Ikeda et al. International Journal for Equity in Healthhttps://doi.org/10.1186/s12939-019-0918-1(2019) 18:15

RESEARCH

Open AccessSocioeconomic inequalities in low backpain among older people: the JAGES
cross-sectional study
Takaaki Ikeda1,2, Kemmyo Sugiyama2*, Jun Aida2, Toru Tsuboya2, Nanae Watabiki3, Katsunori Kondo4,5 andKen Osaka2

Abstract
Background: Low back pain is an important public health issue across the world. However, it is unclear whether
socioeconomic status (SES) is associated with low back pain. This study determines an association between SES andlow back pain among older people.
Methods: We used cross-sectional data derived from the year 2013 across 30 Japanese municipalities. The survey
was conducted between October 2013 to December 2013. Functionally independent community-dwelling older
adults aged 65 and above (n = 26,037) were eligible for the study. Multilevel Poisson regression analysis with arobust variance estimator was used to examine the association between SES and low back pain. Self-reported low
back pain in the past year was used as a dependent variable. Educational attainment, past occupation, equivalized
household income, wealth, and subjective economic situation represented SES and were separately analyzed as
independent variables, adjusted for covariates including age and sex.
Results: The prevalence of low back pain was 63.4%. Overall, lower SES were more likely to suffer from low backpain compared with that for the highest. First, as for the educational attainment, the prevalence ratio (PR) (95%credible interval (CI)) for the lowest level was 1.07 (1.02–1.12). Second, as for the past occupation, the PR (95% CI)
for the blue-collared workers compared with professionals was 1.06 (1.01–1.11). Third, as for the equalized
household income, the PRs (95% CI) for lower middle and the lowest income levels were 1.08 (1.02–1.13) and 1.16
(1.10–1.23), respectively. Fourth, as for the wealth, the PRs (95% CI) for lower middle and the lowest wealth levels
were 1.11 (1.04–1.19) and 1.18 (1.11–1.27), respectively. Fifth, as for the subjective economic situation, the PRs (95%
CI) for lower middle and the lowest financial conditions were 1.18 (1.10–1.26) and 1.32 (1.22–1.44), respectively.
Conclusions: Significant socioeconomic inequalities were observed in low back pain among older individuals inJapan. Policymakers and clinicians must understand the nature of these inequalities.
Keywords: Income, Educational attainment, Subjective economic situation, Occupation, Low back pain,
Socioeconomic status, Health inequalities* Correspondence: ksugiyama@med.tohoku.ac.jp
2
Department of International and Community Oral Health, Tohoku University
Graduate School of Dentistry, 4-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi
980-8575, Japan
Full list of author information is available at the end of the article© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Ikeda et al. International Journal for Equity in Health(2019) 18:15

Introduction
Low back pain is the number one cause of disability [1]. It
is also most commonly experienced among musculoskeletal pains for all age groups [2, 3]. As a whole, musculoskeletal pains impact individuals’ diseases and functional
status such as depression [4], dementia [5], falls [6], anddisability [5]. Based on the systematic review of the prevalence of low back pain in the adult populations, theestimated one-year prevalence was 38.0% ± 19.4% andmore likely to be higher in the older populations [2].
Socioeconomic inequalities in health among older
populations have emerged as a global concern [7, 8].
Recent studies have reported that such inequalities wereobserved not only in diseases but also in symptoms,
including musculoskeletal pains [9–12]. Various studies
reported socioeconomic inequalities in the risk factors of
low back pain [13–16] such as depression [17], obesity[18], and smoking [18]. However, the results of previous
studies on socioeconomic status (SES) and low back pain
have been inconsistent. A recent large-scale cross-sectional study from the United States reported that thelowest income levels are significantly associated with low
back pain compared with the highest income levels [12].
On the other hand, another cross-sectional study from
France reported that there was no association between
educational attainment and low back pain [19]. The
difference in results might be explained by the different
aspects of SES indicators; income is a proxy of thepresent SES and education is a proxy of the past SES.
Seldom studies have investigated the associations between various SES factors and low back pain. Here, we
conducted a cross-sectional study to determine the association of past and present SES with low back pain
among older Japanese people.

Fig. 1 Recruitment diagram of the complete data

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Methods
Study population

We used data from the Japan Gerontological Evaluation
Study (JAGES) project, which was cross-sectional data
derived from the year 2013. Self-reported questionnaires
were mailed to 112,123 people aged ≥65 years, who were
not part of the long-term care insurance system [20].
Based on official residential registers obtained fromrespective municipal governments, the questionnaires
were randomly mailed to residents selected from the17-city areas, all of which have larger populations. In theother 13 municipalities, all of which have a smaller
population, questionnaires were mailed to all eligible
residents. The survey was conducted between October
2013 and December 2013. The questionnaires weredivided into five subsets because many items wereinquired as the whole questionnaire. The 112,123 eligible
individuals were each distributed one of the five questionnaire subsets. Therefore, 38,724 individuals were
mailed the questionnaire that included questions on low
back pain. Of them, 27,684 individuals responded, with a
response rate of 71.5%. Consequently, we used the datafrom 24,285 individuals in the analysis (see Fig. 1).
Dependent variables: Low back pain in the past year

To measure chronic pain rather than acute one, themost widely used period is one-year prevalence of low
back pain in previous studies [2]. Although a previous
cohort study targeted at older people examined the association between pain intensity within one month andincident disability [5], we consider that such relatively
acute pain might not have been enough when considering the long-term mechanism of disability. Therefore, weused one-year prevalence of low back pain. Information

Ikeda et al. International Journal for Equity in Health(2019) 18:15

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on low back pain was obtained by asking the following
question to the participants: “Have you felt pain in or
around your lower back in the past year?” Responses of“yes” indicated the presence of low back pain. If individuals responded “yes” in the previous question, we further
obtained information on the severity of low back pain
by asking the following question: “Have you felt physically limited in your daily life because of low backpain?” Responses of “yes” indicated the presence ofintense low back pain in our study. Furthermore, we
obtained information on medical access for low backpain by asking the following question: “Have you consulted a doctor for low back pain?” with possible answers of “yes” or “no.”“very difficult,” “difficult,” “comfortable,” and “verycomfortable.”

Independent variables: Socioeconomic status

We assessed five SES indicators as independent variables: Educational attainment, past occupation, equivalized household income, subjective economic
situation, and wealth. For older adults, SES can be divided into past or present SES. Thus, we firstly examined the maximum likelihood method with Promax
rotations for factor analysis to detect the type of SES
indicators. Using factor analysis, educational attainment and past occupation were categorized into past
SES. On the other hand, equivalized household income, wealth, as well as subjective economic situation
were categorized into present SES (Additional file 1:
Table S1). Educational attainment, representing past
SES, was categorized into three groups: < 10 years(junior high school; lower secondary education), 10–12 years (high school; upper secondary education),and ≥ 13 years (college or university) [21]. Past occupation, representing past SES, was ascertained by thetype of occupation in which participants had been
engaged for the longest period. Occupations werecategorized as follows: professionals, white-collared
workers except for professionals, blue-collared
workers, and those who had never worked before.
These categories referred to a previous study [22].
We classified equivalized household incomes per year,
as a present SES, into four groups: < 1,000,000 yen,
1,000,000–1,999,999 yen, 2,000,000–2,999,999 yen,and ≥ 3,000,000 yen. Wealth, as a present SES, wasascertained as household assets including savings, realestate (e.g. house, land, condominium), stocks, golf
membership and was classified into five groups: <1,000,000 yen, 1,000,000–4,999,999 yen, 5,000,000–
9,999,999 yen, 10,000,000–49,999,999 yen, and ≥50,000,000 yen. Subjective economic situation, as apresent SES, was ascertained by asking the following
question: “Which of the following best describes
your feelings against your current financial living
conditions as a whole?” with possible answers of

Covariates

We used several covariates on the basis of previous
works: age (65–69, 70–74, 75–79, 80–85, and ≥ 85 years),sex, number of people living together (living alone, livingwith others), marital status [23] (married, widowed,
divorced, and never married), musculoskeletal disease,body mass index (BMI) [15] (< 18.5 kg/m2, 18.5–24.9 kg/
m2, 25.0–29.9 kg/m2, ≥30 kg/m2), drinking habit [24, 25](current, former, never), smoking [16] (current, former,
never), physical activity [26, 27] (≥4 times a week, 2–3
times a week, once a week, 1–3 times a month, a few
times a year and rare), and depression [13, 14] (none,
mild, severe). Following a previous study [26], physicalactivity was measured as comprising the frequency ofmoderately intensive activities such as walking (at a briskpace), dancing, gymnastics, golf, yard work, and car
washing. Drinking habit was ascertained by asking thefollowing question: “Do you drink alcohol?” with possible answers of “current”, “former,” and “never.” The
Japanese short version of Geriatric Depression Scale(GDS), consisting of 15 questions, which has been previously reported to be validated as a screening index for
major depression, was used to assess the prevalence ofdepressive symptoms [28, 29]. We classified the participants into three groups: those with non-depressive
symptoms (GDS < 5), those with mild depression(GDS of 5–9), and those with severe depression (GDS
≥10) [29].
Statistical analysis

Considering the hierarchical structures of municipalities
compared to individuals, multilevel Poisson regression
analysis with a robust variance estimator was used to
examine the association between SES and low back pain[30]. The data was structured as two levels: individual aslevel 1 and municipality as level 2. Estimates wereobtained from Bayesian estimation using Markov Chain
Monte Carlo (MCMC) methods. To avoid multicollinearity, the variables of educational attainment, past occupation, equivalized household income, subjective
economic situation, and wealth were separately analyzed
in different models, adjusting for the covariates. Dummy
variables for all covariates were appropriately added tothe models.
We built four regression models: Model 1, a crude
model; Model 2, with age and sex adjusted to Model 1;
Model 3, with number of persons living together, marital
status, musculoskeletal disease, BMI, drinking habit,smoking, physical activity added to Model 2; Model 4,
with depression added to Model 3. Depression was considered to be a possible intermediate factor in ourIkeda et al. International Journal for Equity in Health(2019) 18:15

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analyses based on previous studies [31, 32]. To evaluate
the social gradient of pain, we also examined P for
trends for each model. Stratified analyses were also performed regarding sex and age (< 75 years old, ≥75 years
old), with reference to previous studies [2, 12]. This
stratification of age was made for the following reason:
copayment for medical services differs between older
adults aged < 75 years and ≥ 75 years in Japan; thus, medical access to low back pain might have been affected byeconomic reasons [33]. Moreover, we performed thesame regression analysis in which SES indicators concurrently analyzed in the same model: Model A, past SES
indicators (educational attainment and past occupation)
concurrently added; Model B, present SES indicators (income, subjective economic situation and wealth) concurrently added; Model C, all SES indicators concurrently
added. All of the previously mentioned models were adjusted for age, sex, number of persons living together,
marital status, musculoskeletal disease, BMI, drinking
habit, smoking and physical activity.
Before performing regression analyses, we employed multiple imputation under the missing at random (MAR) assumption to handle the problem of missing values. Missing
variables were imputed by multivariate imputation chained
equations (MICE) using following variables; sex, age, equivalized household income, educational attainment, past occupation, wealth, subjective economic situation, number ofpeople living together, marital status, presence of knee pain,
presence of low back pain, smoking, drinking habit, BMI,
GDS, physical activity, and residential municipality. On thebasis of a previous work, we imputed not only independent
variables and covariates, but also dependent variable [34].
Rubin’s rule was used to combine the results across 10 imputed datasets [35]. For the complete case analysis, we usedlistwise deletion methods.
For sensitivity analysis, we performed the same analysis among participants who subjectively reported physical limitation due to low back pain in daily life (n =7878) [36], as intense low back pain might shorten
healthy life expectancy [37]. Multilevel analyses were
performed with MLwiN, version 3.02 (Centre for Multilevel Modelling, University of Bristol) via Stata, version
15.1 (Stata Corp, College Station, TX). All other analyses
were conducted using Stata.

income level and with lower wealth were more likely tosuffer from low back pain in the past year (Table 1).

Results
Demographic characteristics

Table 1 and Additional file 1: Table S2 summarize thedemographic characteristics, health status, and healthbehaviors of all eligible participants, respectively. The
prevalence of low back pain in the past year was 63.4%
in the complete data. (Additional file 1: Table S2) Those
who were older, female, living alone, less educated, lower

Back pain and socioeconomic status

Table 2 summarizes the results of multilevel Poisson regression analyses for the five SES independent variables
after imputation for missing data. The municipality levelvariances were small in all models (Table 2).
First, as for educational attainment, after adjusting forcovariates and risk factors (Model 3), those of the lowest
educational level were more likely to experience lowback pain compared with the highest educational level–a
prevalence ratio (PR) (95% credible interval (CI)) of 1.07
(1.02, 1.12). This association was attenuated after additional adjustment for depression–a PR (95% CI) of 1.05
(1.002, 1.10) (Model 4). Second, as with past occupation,
the PR (95% CI) of experiencing low back pain forblue-collared workers compared with professionals was
1.06 (1.01, 1.11). This association was attenuated after
additional adjustment for depression–a PR (95% CI) of
1.04 (1.001, 1.10) (Model 4). Third, with regard to equivalized household income, after adjusting for covariates
and risk factors (Model 3), the PRs (95% CI) for lower
middle and the lowest income levels were 1.08 (1.02,
1.13) and 1.16 (1.10, 1.23), respectively. Significant associations persisted after controlling for additional adjustment for depression (Model 4). Fourth, with regard to
subjective economic situation, Model 3 showed that thePRs (95% CIs) for the “difficult” and the “very difficult”
situations were 1.18 (1.10, 1.26) and 1.32 (1.22, 1.44), respectively. The associations similarly persisted in Model
4. Finally, with regard to wealth, Model 3 showed thatPRs (95% CIs) for the lower middle and the lowest
wealth levels were 1.11 (1.04, 1.19) and 1.18 (1.11, 1.27),
respectively. The associations similarly persisted inModel 4. P for trends in education, past occupation, income, subjective economic situation, and wealth weresignificant (Table 2). All results using complete data
were similar to those from multiple imputation pooleddata (Additional file 1: Table S3).
Additional file 1: Table S3 also shows the associationsof covariates and risk factors with low back pain. Overall, being older, female, the presence of musculoskeletal
pain, obesity, and depression were associated with low
back pain in several models. As for BMI, both overweight and obesity were associated with low back pain
compared with normal weight (Model 3). For example,
when SES was determined by income level, PRs (95%
CI) for overweight and obesity were 1.07 (1.02, 1.12) and1.16 (1.02, 1.32), respectively. The associations persisted
after additional adjustment for depression (Model 4).
Similarly, as for depression, both mild and severe depression were associated with low back pain comparedwith non-depression (Model 3). When SES was

Ikeda et al. International Journal for Equity in Health(2019) 18:15

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Table 1 The presence of low back pain in participants by characteristic (n = 26,037)
Characteristics

Total

Having low back pain

N

N (%)

Male

12,0886892 (61.0)

Female13,9498509 (65.5)

PvalueSexAge, years<.01

74.02 (6.25) a

65–69

7220

4155 (60.5)

70–74

7796

4479 (61.3)

75–79

5754

3439 (64.8)

80–84

3519

2229 (69.0)

≥ 85

1748

1099 (69.8)

10,8476602 (66.0)<.01

Educational attainment, years< 10 (junior high school; lower secondary education)
10–12 (high school; upper secondary education)

9509

5597 (62.4)≥ 13 (college or university degree)

5072

2866 (59.3)

3742

2179 (59.4)<.01

Past occupation
Professionals
White-collared workers

5305

3150 (60.4)

Blue-collared workers

9627

6010 (64.3)

Never worked before1302

826 (65.5)< 1 million

3119

2107 (70.3)

1 million–1.99 million

7735

4795 (63.7)

2 million–2.99 million

4819

2839 (60.3)≥ 3 million

4974

2863 (58.6)

Very difficult

19631378 (76.0)

Difficult8853

5556 (67.2)

Comfortable

12,2476871 (59.8)

Very comfortable

2286

1225 (56.5)< 1 million

2101

1428 (70.3)

1 million–4.99 million

2815

1800 (65.8)<.01Equivalized household income, yen<.01

Subjective economic situation<.01

Wealth, yen

5 million–9.99 million

3255

1971 (62.0)

10 million–49.99 million

7842

4693 (61.0)≥ 50 million

2828

1586 (57.2)

Non (GDS < 5)

15,5928578 (58.2)

Mild (GDS of 5–9)4178

2834 (71.9)

Severe (GDS ≥ 10)1485

1101 (79.6)<.01

Depression<.01amean age (SD). Chi-squared test was performed
1 US dollar is approximately 100 yen and 1 EURO is approximately 130 yen

determined by income level, PRs (95% CI) for mildand severe were 1.19 (1.13, 1.26) and 1.29 (1.19,
1.40), respectively. Additional file 1: Table S4 shows
the results of stratified analyses. When considering

sex stratification, sex differences were observed regarding education, past occupation, and equivalized
household income–the associations were observed
among males. When considering age stratification, noIkeda et al. International Journal for Equity in Health(2019) 18:15

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Table 2 The association of each socioeconomic status with low back pain after multiple data imputations (n = 26,037. Multilevel
Poisson regression analysis)
Socioeconomic status

Model 1Model 2Model 3Model 4PR

95% CIPR

95% CIPR

95% CIPR

95% CI

10–12

1.05

1.01, 1.10

1.04

0.99, 1.09

1.03

0.99, 1.09

1.03

0.98, 1.08

< 10

1.12

1.07, 1.17

1.09

1.04, 1.14

1.07

1.02, 1.12

1.05

1.002, 1.10

P for trend< 0.01< 0.01< 0.010.01

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

White-collared workers

1.02

0.97, 1.07

1.00

0.95, 1.06

1.01

0.95, 1.06

1.01

0.95, 1.06

Blue-collared workers

1.08

1.03, 1.14

1.06

1.02, 1.11

1.06

1.01, 1.11

1.04

1.001, 1.10

Never worked before

1.11

1.04, 1.19

1.04

0.97, 1.12

1.03

0.95, 1.12

1.01

0.93, 1.09

P for trend< 0.01< 0.01< 0.010.02

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

2 million–2.99 million

1.03

0.98, 1.09

1.03

0.98, 1.08

1.03

0.97, 1.09

1.02

0.97, 1.08

1 million–1.99 million

1.09

1.04, 1.14

1.09

1.04, 1.14

1.08

1.02, 1.13

1.05

1.002, 1.11

< 1 million

1.20

1.14, 1.26

1.17

1.11, 1.23

1.16

1.10, 1.23

1.12

1.06, 1.19

P for trend< 0.01< 0.01< 0.01< 0.010.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

Comfortable

1.06

1.00, 1.12

1.07

1.004, 1.13

1.05

0.99, 1.12

1.04

0.98, 1.11

Difficult

1.19

1.12, 1.26

1.21

1.14, 1.29

1.18

1.10, 1.26

1.14

1.07, 1.22

Very difficult

1.34

1.25, 1.44

1.36

1.26, 1.47

1.32

1.22, 1.44

1.22

1.11, 1.33

P for trend< 0.01< 0.01< 0.01< 0.010.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

10 million–49.99 million

1.07

1.01, 1.12

1.07

1.01, 1.12

1.05

0.99, 1.11

1.04

0.98, 1.10

5 million–9.99 million

1.09

1.03, 1.16

1.09

1.02, 1.15

1.07

1.004, 1.14

1.05

0.98, 1.12

1 million–4.99 million

1.15

1.09, 1.23

1.14

1.08, 1.22

1.11

1.04, 1.19

1.08

1.01, 1.16

< 1 million

1.22

1.15, 1.30

1.21

1.14, 1.29

1.18

1.11, 1.27

1.13

1.06, 1.21

P for trend< 0.01< 0.01< 0.01< 0.010.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

Fixed parameter
Education, years (ref, ≥13)

Random part
Municipality level variance (standard error)
Past occupation (ref, professionals)
Fixed parameter

Random part
Municipality level variance (standard error)
Income, yen (ref, ≥3 million)

Random part
Municipality level variance (standard error)
Subjective economic situation (ref, very comfortable)
Fixed parameter

Random part
Municipality level variance (standard error)
Wealth, yen (ref, ≥50 million)
Fixed parameter

Random part
Municipality level variance (standard error)

Abbreviations: PR prevalence ratio, 95% CI 95% credible interval
Socioeconomic status was separately added to each model. Model 1, a crude model; Model 2, with age and sex adjusted to Model 1; Model 3, with number of
persons living together, marital status, musculoskeletal disease, BMI, drinking habit, smoking and physical activity added to Model 2; Model 4, with depression
added to Model 3

Ikeda et al. International Journal for Equity in Health(2019) 18:15

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Table 3 The association of combined socioeconomic status with low back pain after multiple data imputations (n = 26,037. Poisson
regression analysis)
Socioeconomic status

Model APR

Model B

Model C

95% CIPR

95% CIPR

95% CI

Fixed effect parameters
Educational attainment, years (ref, ≥13)
10–12

1.03

0.98, 1.08

–

–

1.01

0.97, 1.06

< 10

1.05

1.002, 1.11

–

–

1.02

0.97, 1.08

White-collared workers

1.01

0.95, 1.06

–

–

1.01

0.96, 1.07

Blue-collared workers

1.05

0.99, 1.10

–

–

1.03

0.97, 1.08

Never worked before

1.02

0.94, 1.11

–

–

1.00

0.92, 1.08

2 million–2.99 million

–

–

1.00

0.95, 1.06

1.00

0.95, 1.06

1 million–1.99 million

–

–

1.02

0.96, 1.07

1.02

0.96, 1.08

< 1 million

–

–

1.06

0.99, 1.13

1.06

0.99, 1.14

Comfortable

–

–

1.04

0.97, 1.11

1.04

0.97, 1.11

Difficult

–

–

1.12

1.04, 1.20

1.15

1.07, 1.24

Very difficult

–

–

1.17

1.07, 1.29

1.27

1.15, 1.39

10 million–49.99 million

–

–

1.01

0.95, 1.07

1.00

0.94, 1.06

5 million–9.99 million

–

–

0.99

0.93, 1.07

0.99

0.92, 1.06

1 million–4.99 million

–

–

1.01

0.94, 1.09

1.00

0.93, 1.08

< 1 million

–

–

1.03

0.95, 1.12

0.99

0.95, 1.11

Past occupation (ref, professionals)

Income, yen (ref, ≥3 million)

Subjective economic situation (ref, very comfortable)

Wealth, yen (ref, ≥50 million)

Random parameter
Municipality level variance (standard error)

0.001 (< 0.001)

0.001 (< 0.001)

0.001 (< 0.001)

Abbreviations: PR prevalence ratio, 95% CI 95% credible interval
Model A, educational attainment and past occupation concurrently added to the model adjusting for age, sex, number of persons living together, marital status,
musculoskeletal disease, BMI, drinking habit, smoking and physical activity
Model B, income, subjective economic situation, wealth concurrently added to the model adjusting for age, sex, number of persons living together, marital status,
musculoskeletal disease, BMI, drinking habit, smoking and physical activity
Model C, all socioeconomic status concurrently added to the model adjusting for age, sex, number of persons living together, marital status, musculoskeletal
disease, BMI, drinking habit, smoking and physical activity

clear differences were observed between ≥75 years oldand < 75 years old.
Table 3 shows the associations when SES indicators concurrently added to the regression models.
For past SES indicators, the significant association of
educational attainment persisted in the lowest group.
Meanwhile, for past occupation, the association was
no longer statistically significant for blue-collared(Model A). For present SES indicators, the significant associations of subjective economic situation
persisted among difficult and very difficult, while theother associations were attenuated (Model B). When
all SES indicators were included in the model, only
the significant association of subjective economic
situation being difficult and very difficult persisted,while associations were no longer statistically significant for other indicators.

For sensitivity analysis, associations were emphasized for all models when performing the same analysis
among participants who experienced low back pain withlimitations in daily life (n = 7878). (see Additional file 1:
Table S5).

Discussion
To the best of our knowledge, our study was the first to
reveal the association of past and present SES with low
back pain in the older population. We found that participants with low SES, as measured by education, pastoccupation, income, subjective economic situation, andwealth, were more prone to experience low back pain
compared with those with high SES. Moreover, theseresults showed that there was a socioeconomic gradientin low back pain; people with lower socioeconomic background were more likely to suffer from pain. Therefore,Ikeda et al. International Journal for Equity in Health(2019) 18:15

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low back pain is a problem for not only of the deprived
people, but also a problem for the whole society. Expectedly, the associations of SES with low back pain dramatically attenuated when depression was adjusted for.
Regarding present SES, a cross-sectional study fromthe United States found lower-income levels to be associated with low back pain in the general population [12].
This study also indicated that associations between income and low back pain were stronger among males
than among females [12]. The findings of our study arealso in line with those of this cross-sectional study. We
found that older individuals with a lower income level
were more likely to suffer from low back pain. This association was strongly observed among older males.
We also newly elucidated the association between
other present SES, as represented by wealth or subjective
economic situation, and low back pain. Accordingly, we
found that participants with a lower level of both wealthand subjective economic situation were more likely toexperience low back pain, when separately analyzed.
Our further analyses which included all SES factors
showed that the impact of more difficult subjective
economic situation remained significant while the effects
of other SES indicators were attenuated (see Table 3).
Recently, subjective economic situation has been focused upon as a new SES indicator representing theperceived relative deprivation of individuals [38, 39].
A cross-sectional study from Germany showed that
subjective economic situation mediates associations
between objective SES indicators (education, occupation, and income) and depressive symptoms in adults[39]. Moreover, the study reported that the association of subjective economic situation with poor mental health was stronger than that of other SES
indicators [39]. Our findings have the same context
with these results to show that the subjective economic situation had the largest impact. Furthermore,
we revealed that present SES was found to be associated with low back pain among participants aged <75 years as well as ≥75 years. This indicates that
present SES-related inequalities persist throughout thelife.
According to our understanding, this study is among
the first to reveal the associations of past SES, as measured by educational attainment and past occupation,with low back pain among older individuals. We foundthat participants with the lowest educational level andblue-collared workers were more likely to suffer fromlow back pain. Furthermore, the association between
education/occupation and low back pain was stronger
among males than among females. For educational attainment, in contrast to our study, a cross-sectional
study from France that interviewed labor population reported that the association of educational attainment

with low back pain was no longer statistically significant
when adjusting for several lifestyle indicators, including
BMI and smoking [19]. The difference in educational inequalities between studies might be explained as follows:
educational inequalities affect health via health literacy[40], and health literacy is significantly higher in laborgenerations compared with that in older generations[41, 42]. Therefore, such differences between studies
emerged due to demographic differences. No previous
study has investigated the association of occupational
inequalities with low back pain among older populations. However, numerous previous studies have indicated that heavy labor—a common issue faced bymany blue-collared workers—is a risk factor of low
back pain [43–46]. Our study is in accordance withthe results of these prior studies. Similar to present
SES, associations of past SES attenuated when all status indicators were mutually adjusted (see Table 2,Model 4). Furthermore, the association of educational
attainment with low back pain was also observed
among participants aged < 75 years as well as ≥75
years, indicating that educational inequalities persist
throughout the life.
When considering the mechanism of low back pain,
the role of risk factors must be determined. Previous
studies have indicated that depression [13, 14], obesity[15], smoking [16], and lower-income level [12] are risk
factors of low back pain, which is partially in accordancewith our findings. Consistent with the results of a previous study [12], present SES as represented by income,subjective economic situation, and wealth were found to
be statistically associated with low back pain amongolder adults. Two possible pathways exist for present
SES-related inequalities in health: psychosocial stressand material poverty [47]. Subjective economic situationis considered to be a result of income level and isconsidered to represent psychosocial stress rather thanmaterial poverty [47, 48]. Moreover, individuals withlower income levels are more likely to face barriers inaccessing medical care [49]. In our study, among participants with low back pain, medical access to low backpain was significantly different by SES (see Additional
file 1: Table S6). This indicated that barriers in accessing
medical care would be a proxy for material poverty to
account for socioeconomic inequalities in low back pain.
A previous study indicated a mutual effect between
depression and low back pain [14]. Additionally, a causal
relation between low SES and depression has been previously reported [31, 32], which supports our idea ofdepression as an intermediary factor. In addition todepression, numerous earlier studies have reported obesity [15] to be risk factors of low back pain. In our study,
overweight and obesity were associated with low backpain. The associations of obesity somewhat attenuatedIkeda et al. International Journal for Equity in Health(2019) 18:15

Page 9 of 11

when depression was additionally adjusted for. Previous
studies have reported that such adverse health-related factors were strongly related to psychosocial stress [38, 39],
derived from relative deprivation. Therefore, in addition
to depression, obesity might contribute to low backpain through psychosocial stress that is affected by
SES. Furthermore, the association of drinking habit
with low back pain was not statistically significant inour study. However, previous studies have indicatedthat alcohol abuse might be associated with low backpain [24, 25]. We could not identify participants withalcohol abuse; however, alcohol abuse is associatedwith low SES [50].
There are several strengths and limitations of ourstudy. First, we examined the association of past andpresent SES with low back pain. Second, we analyzed
a large sample size (n = 26,037), which is higher thanthat analyzed in previous studies [12, 19]. The firstlimitation of our study is that we were unable to distinguish between acute and chronic pain, which leads
to regression dilution bias. In contrast to chronic
pain, a previous study has shown that individuals witha higher income level were more likely to experience
acute low back pain [12]. Hence, we believe that our
results are under-estimating the associations when
considering such biases. Second, the pain questionnaire we used lacked information on degree of pain.
There is a possibility that inequalities in low backpain might differ in degree of pain. In fact, in oursensitivity analysis, the associations were emphasized
for all models when performing the same regression
analysis among participants who experienced low backpain with limitations in daily life (see Additional file
1: Table S5). Future studies should include question
about degree of pain. Third, we could not clarify thecausal pathway because this is a cross-sectional study.
Thus, the probable mediation by depressive conditions is not always consistent. However, we revealed
that past SES and present SES were associated withlow back pain. Longitudinal or cohort studies are necessary for future studies. Fourth, our study participants were not disabled and were not eligible for theJapanese long-term care insurance system. Future
study is expected to investigate association between
SES and low back pain among population including
those of highly physically limited older people. Fifth,
the generalizability of the present results to the entire
Japanese population remains unclear. This is because
the 30 municipalities investigated in this study were
not randomly selected, and the sampling method forresidents differed according to the population of themunicipality. It was difficult to compare our study
population with the entire older population due tolack of demographic characteristics in national survey.

Conclusion
We analyzed data from a cross-sectional study, revealingthat socioeconomic inequalities were significantly associated with low back pain among the older Japanese population. Policymakers and clinicians must understand thenature of these inequalities.
Additional file
Additional file 1: Table S1. Factor loadings of each socioeconomic
status. Table S2. Health status and health behaviors of all eligible
participants (n = 26,037). Table S3. The associations of each parameterwith low back pain in complete data (n = 24,285. Multilevel Poisson
regression analysis). Table S4. The association of socioeconomic status
with low back pain, stratified by sex or age after multiple data
imputations (n = 26,037. Multilevel Poisson regression analysis). Table S5.
The association of socioeconomic status with severe low back pain,
stratified by sex or age in the complete dataset (n = 16,762. Separately
Multilevel Poisson regression analysis). Table S6. Differences in medical
access for low back pain among participants having low back pain bysocioeconomic status (n = 15,401). (DOCX 119 kb)
Abbreviations
BMI: Body mass index; CI: Credible interval; GDS: Geriatric Depression Scale;
JAGES: Japan Gerontological Evaluation Study; PR: Prevalence ratio;
SES: Socioeconomic status
Acknowledgements
The authors would like to thank all the respondents for their cooperation.
Funding
This study was supported by a grant of the Strategic Research Foundation
Grant-aided Project for Private Universities from the Ministry of Education,
Culture, Sport, Science, and Technology, Japan, 2009–2013, for the Center for
Well-being and Society, Nihon Fukushi University, Grants-in-Aid for Scientific
Research (22330172, 22390400, 22390400, 22592327, 23243070, 23590786,
23790710, 24390469, 24530698, 24653150, 24683018, 25253052, 25870573,
25870881, 26285138, 26882010, 15H04781, 15H01972, 16H05556, 16 K19267)
from the Japan Society for the Promotion of Science. The study was also
supported by a Health and Labour Sciences Research Grant, and grants forComprehensive Research on Aging and Health (H22-Choju-Shitei-008, H24Junkankitou- Ippan-007, H24-Chikyukibo-Ippan-009, H24-Choju- Wakate-009,
H25-Kenki-Wakate-015, H25-Irryo-Shitei-003 [Fukkou], H26-Choju-Ippan-006,
H27-Ninchisyou-Ippan-001, H28-Choju-Ippan-002) from the Ministry of Health,Labour and Welfare, Japan, the Research and Development Grants for Longevity Science from AMED (Japan Agency for Medical Research and development, the Personal Health Record (PHR) Utilization Project from AMED, World
Health Organization Centre for Health Development (WHO Kobe Centre),(WHO APW 2017/713981), Japan Foundation for Aging and Health Research
Support Grant, a grant from The Health Care Science Institute, as well as
grants from National Center for Geriatrics and Gerontology. The researchfunding bodies had no role in the study design, data collection, data analysis,
data interpretation, writing, or submitting of the report.
Availability of data and materials
The data that support the findings of this study are available from the JAGES
project but restrictions apply to the availability of these data, which wereused under license for the current study, and so are not publicly available.
Data are however available from the authors upon reasonable request andwith permission of the JAGES project.
Authors’ contributions
TI designed the study and wrote the initial draft of the manuscript. KS
contributed to the study design and directed its implementation, including
quality assurance and control. JA contributed to analysis and interpretationof data and assisted in the preparation of the manuscript. TT and NW
conducted the literature review and prepare the Methods and the
Discussion sections of the text. KK and KO contributed to data collection. All

Ikeda et al. International Journal for Equity in Health(2019) 18:15

authors contributed to data interpretation, and critically reviewed themanuscript. All authors have approved the final version of the manuscript
and agree to be accountable for all aspects of the work in ensuring thatquestions related to the accuracy or integrity of any part of the work areappropriately investigated and resolved.
Ethics approval and consent to participate
The JAGES protocols were approved by the ethics committee of Tohoku
University and Nihon Fukushi University. We considered that individuals who
responded to our survey had consented to participate.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Department of Rehabilitation, Physical Therapy, Sendai Seiyo Gakuin JuniorCollege, Sendai, Japan. 2Department of International and Community Oral
Health, Tohoku University Graduate School of Dentistry, 4-1, Seiryo-machi,
Aoba-ku, Sendai, Miyagi 980-8575, Japan. 3Sendai Pain Clinic Center, Sendai,
Japan. 4Center for Preventive Medical Sciences, Chiba University, Chiba,
Japan. 5Center for Gerontology and Social Science, National Center for
Geriatrics and Gerontology, Obu, Japan.
Received: 6 September 2018 Accepted: 9 January 2019

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