J Orthop Sci
DOI 10.1007/s00776-015-0730-8

ORIGINAL ARTICLE

The societal burden of chronic pain in Japan: an internet survey
Tomoyuki Takura1 · Takahiro Ushida2 · Tsukasa Kanchiku3 · Nozomi Ebata4 ·Koichi Fujii4 · Marco daCosta DiBonaventura5 · Toshihiko Taguchi3

Received: 17 September 2014 / Accepted: 19 February 2015
© The Japanese Orthopaedic Association 2015

Abstract
Objectives Chronic pain affects between 10–20 % of the
population of Japan and several specific types of chronic
pain have been found to be associated with worse healthoutcomes. The aim of the current study was to investigate
the economic burden of chronic pain as well as the healthstatus among Japanese patients.
Methods Data from the Japan National Health and Wellness Survey (NHWS), a cross-sectional health survey ofadults, were used (N = 30,000). Respondents with chronic
pain (N = 785) were compared with respondents without
chronic pain (N = 29,215) with respect to health status(using the SF-12v2), work productivity and activity impairment (WPAI questionnaire), and healthcare resource use
using regression modeling, controlling for demographic
and health history covariates. Indirect costs were calculatedusing wage rates and the human capital method.
Results Back pain (72.10 %) and shoulder pain/stiffness (54.90 %) were the most prevalent pain types.
Adjusting for demographic and health history differences, respondents with chronic pain reported lower* Marco daCosta DiBonaventura
marco.dibonaventura@kantarhealth.com
1

Department of Health Economics and Industrial Policy,
Osaka University Graduate School of Medicine, Osaka,
Japan

2

Multidisciplinary Pain Center, Aichi Medical University,
Nagakute, Japan3

Department of Orthopaedic Surgery, Yamaguchi University
Graduate School of Medicine, Yamaguchi, Japan

4

Pain/Neuroscience Medical Affairs, Pfizer, Tokyo, Japan

5

Health Outcomes Practice, Kantar Health, 11 Madison
Avenue, 12th Floor, New York, NY 10010, USA

health status [mental component summary (MCS): 44.26
vs. 51.14; physical component summary (PCS): 44.23
vs. 47.48; both p < 0.05], greater absenteeism (4.74 vs.
2.74 %), presenteeism (30.19 vs. 15.19 %), overall work
impairment (31.70 vs. 16.82 %), indirect costs (¥1488,385
vs. ¥804,634), activity impairment (33.45 vs. 17.25 %),physician visits (9.31 vs. 4.08), emergency room (ER) visits (0.19 vs. 0.08), and hospitalizations (0.71 vs. 0.34) (allp < 0.05). Nearly 60 % of respondents with chronic pain
were untreated. The mean level of pain severity in the last
week was 5.26 (using a 0–11 scale); being female, being
elderly, having low income, and having multiple pain types
were significantly associated with greater pain severity (allp < 0.05). Regular exercise was associated with lower pain
severity (p < 0.05).
Conclusions The results suggest that chronic pain has asignificant association in an individual’s health status, work
productivity, daily activity impairment, healthcare resourceuse, and economic burden in Japan. Along with low treatment rates, a multidisciplinary approach may lead to an
improved quality of life and reduce the economic burden
among patients with chronic pain in Japan.

Introduction
The prevalence of chronic pain, defined as pain lasting atleast three months [1], varies between 11.5 and 55.2 %among Western nations [2]. The rates in Japan have beenfound to be similar. A recent (2011) study of 20,000 adults
aged 20–69 years reported that 26.4 % met the criteria
for chronic pain [3]. According to the Institute of Medicine, rates of chronic pain are expected to increase due
to increases in other chronic conditions which co-occur
with pain (e.g., cancer), increases in obesity, improved

13

T. Takura et al.

management of catastrophic injuries (which will reduce
mortality but increase the number of survivors with chronicpain), and greater awareness on the part of the patient aboutchronic pain and treatment options [4].
Evidence from the Western literature suggests significant effects of chronic pain on quality of life, activities of daily living, productivity, suicide risk, and indirect
and direct costs [4–6]. Using a nationwide mailed survey
in Japan, Nakamura et al. [7] found that respondents whoreported experiencing musculoskeletal pain also reported
more work-related effects, more impairment in daily activities, and lower health status as assessed by the Short Form36 (SF-36). Another study using a home visit survey of theelderly in Takasaki City, found that the presence of chronic
knee pain was associated with significantly increased
impairments in activities of daily living [8]. Recently,
DiBonaventura et al. [9] compared respondents with neuropathic pain with controls, adjusting for comorbidities
and other variables using the Japan National Health andWellness Survey, and found that chronic neuropathic pain
was associated with lower health status and greater workrelated impairment and healthcare resource use. Aside fromphysical limitations, other studies have documented therelationship between pain and psychological illness [10].
However, much of the chronic pain research in Japan
has been limited by a focus on specific types of pain (e.g.,
neuropathic, musculoskeletal, knee, etc.), and the evidence of the economic burden of chronic pain among
Japanese patients is quite limited. Therefore, the aim of
the current work was to more broadly examine the typesof chronic pain experienced and its collective burden in
Japan by assessing both humanistic and economic effects.
Specifically, the present study explored the association
between chronic pain and health status, work productivity
loss, healthcare resource utilization, and indirect costs ina nationwide sample. A secondary aim was to understandwhat factors were most strongly associated with greater
pain severity among those with chronic pain.

opt-in survey panels with a total of over 4 million members worldwide. Members of LSR and its partners wererecruited through an opt-in email, co-registration with LSR
partners, an e-newsletter campaigns, and online bannerplacements. All potential panel members must register witha unique email address and password and complete an indepth demographic registration profile.
Panel members were not recruited from a purely convenience-of-sampling standpoint; some attempts weremade to equate the panel membership with basic census
information. For example, in Japan 52 % of the population is female and 32 % of the population reports an annual
income of <¥4 million; 49 % and 31 % of the panel arefemale and have an annual income of <¥4 million, respectively [11]. Nevertheless, the characteristics of the panel do
not entirely match that of the population and skew toward
being a younger population.
The NHWS recruits from this panel in stratified random sample (by sex and age) was implemented to mitigate
some of these biases by ensuring the final NHWS sample isdemographically consistent with the Japanese adult population based on governmental statistics [12]. Specifically,by using the international database of the U.S. Census,
the distribution of age and sex in Japan was identified andmimicked such that the final NHWS sample (N = 30,000;
response rate = 15.33 %) matches these distributions.
Although not included in the sampling frame, distributions of household income and region are also consistent
between the NHWS data and the governmental statistics ofJapan [12]. All respondents provided informed consent andthe study protocol was reviewed and approved by an institutional review board. Respondents received modest compensation in exchange for their participation.
Sample
The total sample in the 2011 Japan NHWS is 30,000
respondents. All respondents were included in the analyses.
Measures

Methods
Chronic pain
Data source and procedures
Data from the 2011 Japan National Health and Wellness
Survey (NHWS) were used in the analyses. The NHWS
is an Internet survey administered to adults in Japan from
November to December, 2011. The NHWS is a purely
patient-reported survey which includes information onsociodemographics, general health history, medical comorbidities, medication usage, and health outcomes, among
other variables. NHWS respondents are members of theLightspeed Research (LSR) panel or its partners, which are

13

All respondents in the NHWS were asked whether theyexperienced pain in the past 12 months. For those who
answered affirmatively, they were then asked if they experienced pain in the past month. For those who answered
affirmatively, they were presented with a list of types ofpain and asked which they had experienced and whether
those types of pain were diagnosed. If they were diagnosed, respondents were asked how long they experienced
each type of pain. From all this information, a dichotomous
group variable (having chronic pain vs. not having chronic

The societal burden of chronic pain…

pain) was used as the primary predictor of study outcomes.
Chronic pain was defined as experiencing one of the following types of pain for three months or more: arthritis,back problems, cancer, fibromyalgia, joint, neck, neuropathic (including diabetic peripheral neuropathy), postherpetic neuralgia, shoulder, sprain/strain, surgery/medical
procedure, or phantom limb pain. Those that did not meetthe definition for chronic pain were considered to not havechronic pain. Patients with chronic pain were asked to evaluate their level of severity for each type of pain (“mild”,“moderate”, or “severe”) and also asked to provide an overall assessment of their pain severity in the last week using
an 11-point numeric rating scale (NRS-11; with 0 representing no pain, to 10 representing the worst pain imaginable). Patients were also asked whether they were currently
taking a prescription medication at the time of the survey(yes vs. no).
Sociodemographics
The sex, age, marital status (married/living with partner vs. not-married), education (university educated vs.
less than university educated), annual household income(<¥3,000,000, ¥3,000,000 to <¥5,000,000, ¥5,000,000
to <¥8,000,000, ≥¥8,000,000, or decline to answer), andemployment status (currently employed or not currently
employed) were assessed and considered as covariates.
Household composition (living alone, living with adults
only, living with children and adults, or living with childrenonly) was also included as an additional variable in lieu ofmarital status in certain analyses.
Health history
Smoking status (currently smoke vs. do not currently
smoke), alcohol use (currently drink vs. do not currently
drink), and exercise behavior (currently exercise vs. do notcurrently exercise) were assessed and used as covariates
in regression analyses. Height and weight were converted
to body mass index (BMI) with the following categories:<18.5 kg/m2 (i.e., underweight), 18.5 to <25.0 kg/m2 (i.e.,
normal BMI), ≥25.0 kg/m2 (i.e., obese), or decline to provide weight. The Charlson comorbidity index (CCI) was
also included and calculated by weighting the presence ofa variety of chronic conditions and summing the result withgreater total index scores indicating a greater comorbid
burden on the respondent [13].
Health status
The Medical Outcomes Study 12-Item Short Form Survey
Instrument (SF-12v2) is a multipurpose, generic health status instrument [14]. The items from the SF-12v2 are used

to calculate two normed summary scores: physical component summary (PCS) and mental component summary(MCS). The SF-12v2 items can also be used to generate
health state utilities, which represents the preference fora particular health state and varies conceptually from 0 (astate equivalent to death) to 1 (a state equivalent to perfect
health). This conversion is achieved through applicationof the SF-6D classification [15]. For all SF-12v2-derived
measures, higher scores indicate better health status.
Work productivity and activity impairment
Work productivity was assessed using the general healthversion of the Work Productivity and Activity Impairment(WPAI-GH) questionnaire, a 6-item validated instrument,
which consists of four metrics: absenteeism (the percentage of work time missed because of one’s health in the pastseven days), presenteeism (the percentage of impairment
experienced while at work in the past seven days becauseof one’s health), overall work productivity loss (an overall
impairment estimate that is a combination of absenteeism
and presenteeism), and activity impairment (the percentage
of impairment in daily activities because of one’s health in
the past seven days) [16]. Absenteeism, presenteeism, andoverall work productivity loss were only calculated for
respondents who were employed.
Healthcare resource use
Healthcare utilization was defined by the number of traditional healthcare provider visits, the number of emergency room (ER) visits (“How many times have you been
to the ER for your own medical condition in the past six
months?”), and the number of times hospitalized (“Howmany times have you been hospitalized for your own medical condition in the past six months?”) for all medical conditions experienced in the past six months.
Statistical analyses
Differences between those with and without chronic pain
were examined with respect to demographics and healthcharacteristics using Chi square tests (categorical outcomes) and analysis of variance (ANOVA) tests (continuous outcomes). Differences between those with and without chronic pain were then examined with respect to healthoutcomes (i.e., health status, work productivity and activity
impairment, and healthcare resource use) using regression
modeling, controlling for age, gender, household income,
BMI, smoking status, exercise behavior, and the CCI. These
covariates were chosen because they differed between thosewith chronic pain and those without chronic pain and have
known associations with health outcomes in Japan [12].

13T. Takura et al.

General linear models were used for health status variables
due to the normality of the distribution. Aside from reporting the unstandardized regression estimate (b) for the effectof pain (which indicate the difference in the dependent variable between those with and without chronic pain, holding
all covariates constant), adjusted means were also reported
from these models using a least squares algorithm, setting
each covariate to the mean of the analytical sample. Generalized linear models were used for work productivity andhealthcare resource use variables due to their pronounced
skew. A negative binomial distribution provided the best fit
for the work productivity, activity impairment, and healthcare resource use variables due to the skewed distributions
and a multiplicative dispersion parameter being added toadjust the standard errors because of a slight model underdispersion. Unstandardized regression estimates (b) as wellas the anti-log of b (i.e., eb or rate ratios) were presented forthe effect of pain. Rate ratios represent the multiplicative
factor for which the mean of the pain group is greater thanthat of the control group (e.g., a rate ratio of 1.30 indicatesthe mean for the pain group is 1.30 times that of the control group). In addition, adjusted means were also reported
from these models using a maximum likelihood algorithm,
setting each covariate to the mean of the analytical sample.
Annual costs due to absenteeism and presenteeism were
calculated by integrating information from the WPAI andhourly wage rates from the Japan Basic Survey on Wage
Structure [17] using the human capital method. For each
employed respondent, his or her hours lost due to either
absenteeism or presenteeism (measured using the WPAI
instrument) were then multiplied by their estimated wage
(based on age and sex) to estimate total weekly indirect
costs. These figures were then annualized. The presence ofchronic pain was then used to predict these total indirect
costs using a generalized linear model (again, with a negative binomial distribution and a log-link function to best
capture the skewness of the data) controlling for age, sex,
household income, BMI, smoking status, exercise behavior, and the CCI. Adjusted means were reported from thesemodels using a maximum likelihood algorithm, setting
each covariate to the mean of the analytical sample.
To further illustrate the burden of chronic pain, an
additional analysis was performed comparing those withchronic pain to those with non-chronic pain (i.e., those
without any pain from the control group in the main analysis were excluded from the control group in this supplemental analysis). This approach would isolate the effectof chronic pain on health outcomes above and beyond any
effect of general non-chronic pain. Aside from excluding respondents without pain from the control group, this
supplemental analysis replicated the exact approach in themain analysis described above. The same outcomes were
examined and the same modeling procedure was used.

13

To examine the secondary objective, overall pain severity in the last week was predicted using the available sociodemographic and health history variables in a general linear model. In this model, the same covariates were codedand used as described above with the following exceptions:age was categorized in ten-year increments as opposed toentered continuously (to best understand the age groups
with the highest pain severity), household composition wasused in lieu of marital status, and the number of pain types(1, 2, 3, or 4+) was used as an additional predictor.
All analyses were conducted using SAS version 9.3(Cary, NC).

Results
A total of 785 respondents met our criteria for chronic
pain (2.62 %). Demographic and health characteristics forrespondents with and without chronic pain are shown in
Table 1. Respondents with chronic pain were significantly
more likely to be older, be female, be unemployed, exercise
regularly, and have a greater BMI and comorbidity burden
compared with those without chronic pain (all p < 0.05).
Among respondents who reported chronic pain, thetypes of pain experienced in the past month and typesof pain diagnosed are shown in Table 2. Both back pain
(72.10 %) and shoulder pain/stiffness (54.90 %) were themost prevalent pain types and were experienced by morethan half of respondents with chronic pain. A total of
41.02 % respondents reported they were currently using aprescription medication for their pain. However, there wasconsiderable variability in treatment rates across pain types,
as shown in Table 2. Despite the small sample size (which
creates additional uncertainty around these estimates), broken bones (71.43 %), cancer pain (66.67 %), and phantom
limb pain (66.67 %) were the most likely pain types to betreated. Conversely, menstrual cycle pain (38.60 %), backpain (40.81 %), and post-herpetic neuralgia (40.91 %) were
the pain types least likely to be treated.
Analyses were then conducted to compare the healthoutcomes of those with and without chronic pain using
regression analysis. Respondents with chronic pain
reported significantly lower levels of MCS [b (unstandardized regression estimate) = −3.24, p < 0.05] and PCS(b = −6.88, p < 0.05) relative to respondents without
chronic pain (both p < 0.05). Figure 1 shows the adjusted
means for MCS (44.26 vs. 51.14 for chronic pain andcontrols, respectively) and PCS scores (44.23 vs. 47.48
for chronic pain and controls, respectively). Additionally, those with chronic pain reported significantly lower
levels of health utilities (b = −0.09, p < 0.05; Adjusted
means = 0.68 vs. 0.77 for those with and without chronic
pain, respectively).

The societal burden of chronic pain…
Table 1  Demographic and health characteristics in those with and without chronic painAge (years)
Mean ± SD
GenderFemale (%)
Male (%)
Education levelLess than university education (%)
University education or higher (%)
Household composition
Live alone (%)
Live with children only (%)
Live with adults only (%)
Live with adults and children (%)
Annual household income<¥3 million (%)¥3 million to <¥5 million (%)¥5 million to <¥8 million (%)¥8 million or more (%)
Decline to answer (%)
Employment status
Not currently employed (%)
Employed (%)
Body mass index (BMI) category
Underweight (%)
Acceptable risk (%)
Increased risk (%)
High risk (%)
Decline to provide weight (%)
Alcohol use
Do not drink (%)
Drink alcohol (%)
Smoking behavior
Never smoked (%)
Former smoker (%)
Current smoker (%)
Exercise behavior
Do not exercise (%)
Regularly exercise (%)
Charlson comorbidity index
Mean ± SD

Total (N = 30,000)

Chronic pain (N = 785)

No chronic pain (N = 29,215)p

47.38 ± 15.63

53.97 ± 13.78

47.20 ± 15.64<0.001

14,958 (49.9 %)15,042 (50.1 %)428 (54.5 %)357 (45.5 %)14,530 (49.7 %)14,685 (50.3 %)

0.008

15,912 (53.0 %)14,088 (47.0 %)422 (53.8 %)363 (46.2 %)15,490 (53.0 %)13,725 (47.0 %)0.683

4466 (14.9 %)375 (1.3 %)17,538 (58.5 %)7621 (25.4 %)

111 (14.1 %)
15 (1.9 %)
485 (61.8 %)
174 (22.2 %)4355 (14.9 %)
360 (1.2 %)17,053 (58.4 %)7477 (25.5 %)

0.047

5143 (17.1 %)7571 (25.2 %)7664 (25.5 %)6586 (22.0 %)3036 (10.1 %)

152 (19.4 %)
204 (26.0 %)
179 (22.8 %)
201 (25.6 %)49 (6.2 %)4991 (17.1 %)7367 (25.2 %)7485 (25.6 %)6385 (21.9 %)2987 (10.2 %)<0.001

12,180 (40.6 %)17,820 (59.4 %)

365 (46.5 %)
420 (53.5 %)11,815 (40.4 %)17,400 (59.6 %)<0.0013131 (10.4 %)15,197 (50.7 %)8309 (27.7 %)2147 (7.2 %)1216 (4.1 %)

65 (8.3 %)
350 (44.6 %)259 (33.0 %)98 (12.5 %)
13 (1.7 %)3066 (10.5 %)14,847 (50.8 %)8050 (27.6 %)2049 (7.0 %)1203 (4.1 %)<0.0018485 (28.3 %)21515 (71.7 %)

217 (27.6 %)568 (72.4 %)8268 (28.3 %)20947 (71.7 %)

0.687

16780 (55.9 %)7057 (23.5 %)6163 (20.5 %)375 (47.8 %)
241 (30.7 %)
169 (21.5 %)16405 (56.2 %)6816 (23.3 %)5994 (20.5 %)<0.001

16443 (54.8 %)13557 (45.2 %)372 (47.4 %)
413 (52.6 %)16071 (55.0 %)13144 (45.0 %)<0.0010.15 ± 0.58

0.49 ± 1.82

0.14 ± 0.50

<0.001Those with chronic pain also reported significantly
higher levels of absenteeism (b = 0.55, rate ratio = 1.73,
p < 0.05), presenteeism (b = 0.69, rate ratio = 1.99,
p < 0.05), overall work impairment (b = 0.63, rate
ratio = 1.89, p < 0.05), and activity impairment (b = 0.66,
rate ratio = 1.94, p < 0.05). Figure 2 shows the associatedadjusted means for these outcomes (absenteeism: 4.74 vs.

2.74 %; presenteeism: 30.19 vs. 15.19 %; overall work
impairment: 31.70 vs. 16.82 %; and activity impairment:
33.45 vs. 17.25 %) for those with and without chronic pain,
respectively (all p < 0.05).
Differences in absenteeism and presenteeism werethen converted to annual costs in Fig. 3. Adjusting forconfounding variables, employed patients with chronic

13

T. Takura et al.
Table 2  Types of pain experienced, diagnosed, and treated, and levels of pain severity among those with chronic pain (N = 785)
Pain type

Experienced in thepast month (among
all patients,
N = 785)

Back problems
Shoulder pain/stiffness
Joint pain
Neck
Arthritis
HeadacheMigraine
Dental problems
Menstrual cycle
Sprains/strains
Neuropathic pain (including DPN)
Post-herpetic neuralgia
Surgery/medical procedure
Fibromyalgia
Broken bones
CancerPhantom limb pain*

Moderate

Severe

%

n

%

n

%

n

%

n

%

566
431
252
208
191
164
115
57
61
50
26
22
22
18
14
6

72.10
54.90
32.10
26.50
24.33
20.89
14.65
13.32
7.77
6.37
3.31
2.80
2.80
2.29
1.78
0.76

231
179
108
98
80
77
56
22
25
23
13
9
15
8
10
4

40.81
41.53
42.86
47.12
41.88
46.95
48.70
38.60
40.98
46.00
50.00
40.91
68.18
44.44
71.43
66.67

477
233
180
112
154
53
46
16
45
31
29
18
21
22
12
6

60.76
29.68
22.93
14.27
19.62
6.75
5.86
3.74
5.73
3.95
3.69
2.29
2.68
2.80
1.53
0.76

241
185
128
90
111
91
51
45
29
39
12
14
9
9
7
4

42.58
42.92
50.79
43.27
58.12
55.49
44.35
73.77
50.88
78.00
37.50
63.64
40.91
45.00
50.00
66.67

253
185
100
90
63
64
53
13
17
8
16
5
6
8
7
2

44.70
42.92
39.68
43.27
32.98
39.02
46.09
21.31
29.82
16.00
50.00
22.73
27.27
40.00
50.00
33.33

72
61
24
28
17
9
11
3
11
3
4
3
7
3
0
0

12.72
14.15
9.52
13.46
8.90
5.49
9.57
4.92
19.30
6.00
12.50
13.64
31.82
15.00
0.00
0.00

6

0.76

4

66.67

3

0.38

2

33.33

3

50.00

1

16.67

40

*

30
20
10

PCS

Chronic pain (N=785)

No chronic pain (N=29,215)

*

35

*

*

30
25
20
15
10

*

5
0

MCS

Mild

n

40

0

Severity of pain experienced in the past
month

%

Chronic pain (N=785)
No chronic pain (N=29,215)50

Diagnosed(among all
patients,
N = 785)nAdjusted impairment (WPAI)

Adjusted health status (SF-12v2)

60

Using a prescription
medication (among
patients with that particular type of pain)

Absenteeism %Presenteeism %Overall work
impairment %Activity
impairment %Fig. 1  Adjusted means of physical and mental health component summary scores among those with and without chronic pain.
*p < 0.05 relative to the no chronic pain group (based on a t test of
the chronic pain parameter estimate in the general linear model);error bars represents 95 % confidence intervals. PCS physical component summary and MCS mental component summary of the SF12v2. All models controlled for age, sex, household income, bodymass index, smoking status, alcohol use, exercise behavior, and theCharlson comorbidity index

Fig. 2  Adjusted means of work productivity and activity impairment among those with and without chronic pain. *p < 0.05 relative
to the no chronic pain group (based on a z test of the chronic pain
parameter estimate in the generalized linear model); error bars represents 95 % confidence intervals. Only those employed full or parttime (n = 17,820) provided data for absenteeism, presenteeism, andoverall work impairment. All models controlled for age, sex, household income, body mass index, smoking status, alcohol use, exercise
behavior, and the Charlson comorbidity index

pain had ¥232,815 in lost wages due to absenteeism perpatient per-year compared with ¥139,330 for employed
respondents without chronic pain (p < 0.05). Similarly,
employed patients with chronic pain had ¥1,255,570 in lost

wages due to presenteeism per-patient per-year comparedwith ¥665,304 for employed respondents without chronic
pain (p < 0.05). The total indirect costs for employedpatients with chronic pain were significantly higher than

13

The societal burden of chronic pain…
¥18,00,000
Presenteeism

Adjusted indirect costs¥16,00,000

Absenteeism¥14,00,000¥12,00,000¥10,00,000¥12,55,570 *¥8,00,000¥6,00,000¥6,65,304¥4,00,000¥2,00,000¥0¥2,32,815 *¥1,39,330

Employees with chronic
pain (N=420)

Employees without chronic
pain (N=17,400)

Fig. 3  Adjusted means of indirect costs among those with and without chronic pain. *p < 0.05 relative to the no chronic pain group(based on a z test of the chronic pain parameter estimate in the generalized linear model); error bars represents 95 % confidence intervals.
All models controlled for age, sex, household income, body massindex, smoking status, alcohol use, exercise behavior, and the Charlson comorbidity index

Adjusted resource use events in the past 6 months

employed respondents without chronic pain (¥1,488,385
vs. ¥804,634, p < 0.05).
Similar relationships were observed when predicting healthcare resource use. Chronic pain was associatedwith an increased number of physician visits (b = 0.83,
rate ratio = 2.29, p < 0.05), ER visits (b = 0.86, rate
ratio = 2.37, p < 0.05), and hospitalizations (b = 0.74, rate
ratio = 2.10, p < 0.05). The adjusted means of these models
are shown in Fig. 4. In all cases, the adjusted means weremore than twice as high for respondents with chronic pain:
physician visits = 9.31 vs. 4.08, ER visits = 0.19 vs. 0.08,
and hospitalizations = 0.71 vs. 0.34 (all p < 0.05).
12

10

Chronic pain (N=785)*

No chronic pain (N=29,215)

Severity of pain experienced in the past month for eachspecific pain type was examined among those with chronic
pain, as shown in Table 2. For most types of pain, between
40–60 % of respondents reported their pain as moderateto-severe. The highest proportion of severe pain was
experienced in relation to surgery/medical procedure pain
(31.82 %). A majority of respondents experiencing chronic
pain reported not being treated for their pain (n = 463,
58.98 %).
As discussed in the statistical analysis section above,a supplemental analysis was performed to compare
health outcomes between respondents with chronic
pain (N = 785) and respondents with non-chronic pain
(N = 3219). Adjusting for the same set of covariates as
noted in the main analysis, a similar pattern of results wasobserved (see Table 3). Although adjusted MCS scores
were not significantly different between groups (44.04 vs.
44.41 for chronic pain and non-chronic pain, respectively;
p = 0.38), PCS scores and health utilities were significantly
lower among those with chronic pain (both p < 0.05).
Similarly, all forms of work impairment, activity impairment, and healthcare resource use were significantly higher
among those with chronic pain compared with those with
non-chronic pain (all p < 0.05). Levels of impairment andhealthcare resource use were generally 50 % higher among
those with chronic pain.
Further, employed patients with chronic pain had¥218,424 in lost wages due to absenteeism per-patientper-year compared with ¥121,339 for employed respondents with non-chronic pain (p < 0.05). Similarly, employedpatients with chronic pain had ¥1,300,077 in lost wages
1.6

*

1.4
1.2

8

1
0.8

6

0.6

4

0.4
2

0

*

0.2

Provider visits

Fig. 4  Adjusted means of healthcare resource use among those withand without chronic pain. *p < 0.05 relative to the no chronic paingroup (based on a z test of the chronic pain parameter estimate in
the generalized linear model); error bars represents 95 % confidence0

ER visits

Hospitalizations

intervals. All models controlled for age, sex, household income, bodymass index, smoking status, alcohol use, exercise behavior, and theCharlson comorbidity index

13

T. Takura et al.
Table 3  Adjusted means of health outcomes between those with chronic pain and those with non-chronic pain
Dependent variable

Pain group

Adjusted meanSE

95 % LCL

95 % UCL

p value

SF-12: mental component summary
SF-12: mental component summary
SF-12: physical component summary
SF-12: physical component summary
Health state utility score
Health state utility score
Absenteeism %Absenteeism %Presenteeism %Presenteeism %Overall work impairment %Overall work impairment %Activity impairment %Activity impairment %Healthcare provider visits in past 6 months
Healthcare provider visits in past 6 months
ER visits in the past 6 months
ER visits in the past 6 months
Hospitalizations in the past 6 months

Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain
Non-chronic pain
Chronic pain

44.04
44.41
43.93
47.73
0.67
0.71
4.70
2.60
30.33
20.38
32.02
21.96
34.79
26.07
10.91
6.34
0.20
0.10
0.78

0.38
0.19
0.28
0.14
0.00
0.00
0.98
0.25
1.62
0.51
1.77
0.57
1.17
0.43
0.56
0.16
0.05
0.01
0.22

43.29
44.05
43.38
47.46
0.66
0.70
3.12
2.15
27.32
19.41
28.74
20.87
32.56
25.25
9.86
6.03
0.12
0.08
0.45

44.79
44.78
44.48
48.00
0.68
0.71
7.08
3.14
33.67
21.41
35.68
23.10
37.17
26.93
12.06
6.67
0.33
0.13
1.35

0.3848–<0.0001–<0.0001–
0.0111–<0.0001–<0.0001–<0.0001–<0.0001–
0.0127–0.0356

Hospitalizations in the past 6 months

Non-chronic pain

0.41

0.05

0.31

0.53

–

SE standard error, LCL lower confidence limit, UCL upper confidence limit

due to presenteeism per-patient per-year compared with
¥894,642 for employed respondents with non-chronic pain
(p < 0.05). The total indirect costs for employed patientswith chronic pain were significantly higher than employed
respondents with non-chronic pain (¥1,513,879 vs.
¥1,000,650, p < 0.05).
The overall rating of pain was 5.26 (SD = 2.47) on theeleven-point NRS. We sought to examine which factors
were associated with a higher assessment of severity (seeTable 4). Pain severity was highest among respondents withchronic pain between 30 and 80 years (bs = 1.36–1.77) andlowest among respondents less than 30 years (all p < 0.05).
Females reported higher levels of pain (b = 0.41) as did
respondents with lower household income (b = 0.58; bothp < 0.05). Regular exercise was associated with lower pain
severity (b = −0.43). Although comorbidities were unrelated to pain severity, an increasing number of pain types
was associated with increased severity (2 pain types:b = 0.49; 3 pain types: b = 1.19; 4+ pain types: b = 1.37;
all p < 0.05).

Discussion
Although a few studies have examined the effect of specificchronic pain conditions on the health outcomes of patientsin Japan, the literature has lacked a broad assessment of the13

effect of chronic pain. The aim of the current study was toaddress this gap by examining the effect of chronic pain
across health status, work productivity, healthcare resourceuse, and economic outcomes as well as determining which
factors were most strongly associated with greater pain
severity
A large percentage of chronic pain was experienced in
the back, joints, neck, and shoulders, which replicates previous prevalence studies in Japan [7, 8]. Nearly 60 % ofpatients with chronic pain were not currently treated, highlighting the unmet needs of these patients. A previous study
in Japan found a similar percentage of chronic pain patients
had sought treatment (42 %), however this included both
treatment through a medical institution and folk remedies
and only in patients experiencing chronic musculoskeletal pain [7]. The current finding suggests an inadequacywith current treatment options which has been previously
reported in prior studies in Europe [8, 18].
Very few studies have assessed the burden of chronic
pain in Japan; the ones that have done so were limited byonly including a specific type of pain [7–9]. Comparisons
between those with and without chronic pain suggested,even after adjusting for demographic and health history differences, that patients with chronic pain report worse healthstatus. Chronic pain affects physical functioning, and it is,therefore, not surprising that group differences were greaterfor physical health than on mental health. Nevertheless, for

The societal burden of chronic pain…
Table 4  Predictors of pain
severity among those withchronic pain (N = 785)

Parameterb

95 % LCL 95 % UCL tp

Intercept
Age group: <30 years
Age group: 30 to <40 years
Age group: 40 to <50 years
Age group: 50 to <60 years
Age group: 60 to <70 years
Age group: 70 to <80 years
Age group: 80 years or more
Male
Income: < ¥3 million
Income: ¥3 million to <¥5 million
Income: ¥5 million to <¥8 million
Income: ¥8 million or more
Income: decline to answer
BMI: underweight
BMI: acceptable risk
BMI: increased risk
BMI: high risk
BMI: decline to provide weight
Former smoker
Current smoker
Alcohol use
Regular exercise
CCI
Household composition: live alone
Household composition: live with adults only
Household composition: live with children and adults
Household composition: live with children only
Number of pain types: 1 pain type
Number of pain types: 2 pain types
Number of pain types: 3 pain types

2.97
–
1.36
1.55
1.77
1.50
1.68
0.43
−0.41
0.58
–
0.06
−0.53
−0.07
0.24
–
0.17
0.40
0.24
0.25
0.15
−0.03
−0.43
0.06
–
0.33
0.18
0.59
–
0.49
1.19

1.93
–
0.44
0.69
0.92
0.66
0.78
−1.30
−0.79
0.07
–
−0.42
−1.01
−0.81
−0.40
–
−0.22
−0.15
−1.08
−0.15
−0.32
−0.43
−0.77
−0.04
–
−0.20
−0.44
−0.71
–
0.03
0.70

4.00
–
2.27
2.40
2.61
2.34
2.59
2.17
−0.02
1.10
–
0.54
−0.05
0.68
0.88
–
0.56
0.94
1.56
0.66
0.61
0.36
−0.08
0.15
–
0.86
0.79
1.90
–
0.96
1.69

5.63
–
2.91
3.55
4.10
3.49
3.64
0.49
−2.08
2.21
–
0.24
−2.19
−0.18
0.74
–
0.85
1.43
0.36
1.24
0.62
−0.16
−2.45
1.22
–
1.23
0.56
0.89
–
2.07
4.72

<0.0001–
0.00370.0004<0.0001
0.00050.00030.62210.03770.0273–0.80990.029
0.85890.458–
0.39760.15440.72020.21570.53570.872
0.01470.2235–0.21820.57510.3726–
0.0387<0.0001

Number of pain types: 4+ pain types

1.37

0.91

1.83

5.88

<0.0001b  unstandardized regression estimate, 95 % LCL  95 % lower confidence limit of the unstandardized
regression estimate, 95 % UCL 95 % upper confidence limit of the unstandardized regression estimate,t  t-value

both physical and mental components, differences between
those with and without chronic pain exceeded establishedclinically-relevant cutoffs, emphasizing the dramatic effectof chronic pain on the patient health experience.
By using the WPAI-GH questionnaire, one of the few
ways to understand from the patient perspective how theirhealth is affecting their job duties, this study also provides
an estimate of the economic burden of chronic pain. Compared with those without chronic pain, respondents withchronic pain were characterized by significantly greater
work and activity impairment relative to those without
chronic pain, which is consistent with past research inJapan that focused on work effects and impairment in
activities of daily living [7–9]. Respondents with chronic

pain reported between 73–99 % more work and activity
impairment compared to those without chronic pain; thisequated to approximately one-third more of work time thatwas missed or rendered ineffective due to health problems.
The data additionally showed that respondents with chronic
pain reported more resource use than those without chronic
pain. Chronic pain patients reported over twice the numberof physician visits, ER visits and hospitalizations.
To further reinforce the effect of chronic pain on healthoutcomes, we also performed a supplemental analysis
which compared patients with chronic pain to patients with
non-chronic pain. Although, as would be expected, the differences between groups was less than in the main analysis, significant effects were observed in health status, work

13

T. Takura et al.

impairment, and healthcare resource use. Indeed, respondents with chronic pain reported significantly lower levelsof PCS and health utilities to a clinically relevant degree
and reported approximately 50 % more work impairment
and healthcare resource use visits compared with respondents with non-chronic pain. These results further reinforce
the burden of chronic pain, even above and beyond that ofmore general, non-chronic pain.
The severity results suggest that most patients experience moderate-to-severe levels of pain. Holding other variables constant, income <¥3 million was associated withhigher pain severity. Johannes et al. [19] identified an association of low household income and chronic pain, which
support our current findings. The current study also demonstrated the negative correlation of regular exercise and pain
severity. These results suggest that encouragement of regular exercise by healthcare professionals may help to alleviate the severity of a patient’s pain.
In conclusion, the results suggest that chronic pain has
a significant role in an individual’s health status, work productivity, daily activity impairment, healthcare resourceuse, and economic burden in Japan. Given this burden, andlow treatment rates, the current study suggests that a multidisciplinary approach to patients with chronic pain in Japanis warranted.

Limitations
Because chronic pain could only be calculated for those
who were diagnosed with their type of pain (since durationof pain was not known among those without a diagnosis of
their pain), the prevalence estimate of chronic pain (2.62 %)is dramatically underestimated. However, if we assume the
distribution of pain duration for those diagnosed is similar to all of patients who reported pain in the past month,then the prevalence of chronic pain in the NHWS sample
could be estimated at 12 % (90 % of patients diagnosed had
their pain for 3 months or more ×13.31 % of all NHWS
respondents who reported pain in the past month = 12 %).
This figure of 12 % is more consistent with the literature in
Japan [20].
Other limitations of the current study include a relianceon patient-reported data, the cross-sectional design, statistical methods, and the sampling method. Although patientreported data is necessary to assess the subjective natureof pain, measurement data may have been introduced dueto the inability to verify patient reports of certain variables
such as treatment use or healthcare resource use. Additionally, the cross-sectional nature of the data does not allow
the ability to make causal inferences between the presenceof chronic pain and various outcomes or between healthhistory and pain severity. We attempted to control for a13

variety of key important variables (e.g., age, comorbidities, etc.). However, it is possible there are other third variables not included in the analysis that would explain part,or all, of the association between chronic pain and healthoutcomes. There was also a large imbalance in sample
size between the pain and control groups. Although thisdoes not bias our estimates of the burden of pain, different
adjusted means might be generated if alternative techniques
are used (e.g., matching, instrumental variable approach).
Lastly, it is unclear the extent to which our chronic pain
sample generalizes to the chronic pain population; certain
subpopulations (i.e., patients in very poor health) may beunderrepresented.
Acknowledgments This study was funded by Pfizer Japan, Inc. KF
and NE are currently full-time employees of Pfizer Japan, Inc. The
NHWS is a survey conducted by, with the associated data owned by,
Kantar Health. Pfizer Japan, Inc. purchased access to the NHWS 2011
database and commissioned Kantar Health to analyze the data forthis study and assist in the preparation of this manuscript. MD is afull-time employee of Kantar Health. TTak, TU, TK, and TTag each
received a consultancy fee from Pfizer Japan, Inc. for their participation in the study advisory board.

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