Journal of the National Institute of Public Health 2017Vol.66 No.1 p.75−84＜Original＞
Relationships between medical expenditures and the Specific Health
Checkups scheme in Japan:
A Study of outpatient medical expenditures and questionnaire responses
concerning lifestyle that form part of the Specific Health Checkups scheme in Japan
Yoshimune Hiratsuka 1 ， 2 ），Yoh Tamaki 1 ），Etsuji Okamoto 3 ），Hiroe Tsubaki 4 ），Toshiro Kumakawa 1 ）Department of Health and Welfare Services, National Institute of Public Health
Department of Ophthalmology, Juntendo University School of Medicine
3 ）The University of Fukuchiyama
4 ）National Statistics Center
1）
2）

Abstract
Background: Seven years have passed since the implementation of the Specific Health Checkups andSpecific Health Guidance scheme in Japan, and now it is possible to investigate long-term factors relating
to medical expenditures at a local level by comparing data from Specific Health Checkups with healthinsurance claims. The aim of this study is to investigate long-term relationships between outpatient
medical expenditures and questionnaire responses concerning lifestyle that form part of the Specific
Health Checkups scheme, accumulated during a 5 year period.
Methods: A cumulative total of 43,740 recipients of Specific Health Checkups (representing 14,848
unique individuals) collected between 2008 and 2012, in Mishima City in Shizuoka Prefecture, Japan
were included in this study. The average age was 65.3±7.8 of which 60% were female. Questionnaire
responses concerning lifestyle forming part of the Specific Health Checkups scheme, along with healthinsurance claims data accumulated over 5 years were used. Long-term relationships between outpatient
medical expenditures and patientʼs lifestyle were analyzed using panel data analysis.
Results: Medication of low blood pressure, blood glucose or cholesterol levels, a history of stroke orheart disease along with weight change (gain or loss of ≥3kg) exhibit a relationship with increases in
outpatient medical expenditures. Lifestyle factors such as physical activities, fast walking and good sleep
patterns each displayed relationships with reductions in outpatient medical expenditures. Further, Health
Guidance is also seen to have an association with lowering outpatient medical expenditures in the same
year that the guidance was given, as well as the following year.
Conclusions: Low-cost and easily executable questionnaires as part of the Specific Health Checkups
scheme enable not only risk assessments of lifestyle-disease of the targeted individuals but also theprediction of long-term trends in outpatient medical expenditures. Physical activities, fast walking, good
sleep patterns and Health Guidance have an association with lowering medical expenditures.

keywords: medical expenditures, Specific Health Checkups, Specific Health Guidance, physical activities(accepted for publication, 13th January 2017)

Corresponding author: Yoshimune Hiratsuka.
2-3-6 Minami, Wako, Saitama, 351-0197, Japan.
Tel: 048-458-6338
E-mail: yoshi-h@tkf.att.ne.jp

J. Natl. Inst. Public Health, 66（1）: 201775

Yoshimune HIRATSUKA, Yoh TAMAKI, Etsuji OKAMOTO, Hiroe TSUBAKI, Toshiro KUMAKAWA

I.

Introduction

Each year, national health expenditure is increasing inJapan. During fiscal 1999 national health expenditureexceeded 30 trillion Japanese yen for the first time,currently stands at almost 40 trillion Japanese yen, andthe ratio of national health expenditure to gross domestic
products has reached 8.3% [1]. In particular, medical carecosts for the elderly is growing at the rate of about 9%
annually as the aging population increases rapidly. When
we compare the medical examination fee per person, thefee for the elderly is 5 times greater than that for the nonelderly [2]. Considering these factors, it is necessary that
we continue to promote the improvement and managementof lifelong health as well as to offer adequate medical carefor the elderly in an efficient manner. At the same time,evidence based preventive measures are needed tocontrol the burden placed on society caused by rapidly

increasing medical expenditure .
In 2008, the Japanese Ministry of Health, Labour andWelfare implemented a standard program of healthcheckups to identify persons at high-risk of Metabolic
Syndrome as part of a series of measures carried out toachieve appropriate levels of medical expenditure. Seven
years have passed since the implementation of theSpecific Health Checkups and Specific Health Guidance
scheme. While it is technically possible to investigate
long-term factors relating to medical expenditure at a local
level through the comparison of data relating to Specific
Health Checkups with health insurance claims of medical
fees today, long-term reports of this nature are limited [3, 4].
The Specific Health Checkups scheme uses a low-costand easily executable, standard questionnaire to assess
the lifestyle-disease risks for a patient [5]. Several studies
indicate that answers to 22 simple questions (Table 1) can
help to predict an individual’s state of health [6-9]. If such

Table 1 The Specific Health Checkups standard questionnaire

Q1-3

Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13
Q14
Q15
Q16
Q17
Q18
Q19
Q20
Q21

Q22

76

The Specific Health Checkups standard questionnaire
Questions
Answer choices
Are you currently taking the following medications?
Yes / No
1. a medicine to lower blood pressure
Yes / No
2. insulin injections or a medicine to lower blood glucose
Yes / No
3. a medicine to lower cholesterol
Yes / No
Have you ever been told by a doctor that you have stroke (e.g., cerebral hemorrhage,
Yes / No
cerebral infarction) or have you ever received treatment for stroke?
Have you ever been told by a doctor that you have heart disease (e.g., angina, myocardial
Yes / No
infarction) or have you ever received treatment for heart disease?
Have you ever been told by a doctor that you have chronic renal failure or have you everYes / No
received treatment for chronic renal failure (dialysis)?
Have you ever been told by a doctor that you have anemia?
Yes / NoAre you a current regular smoker?
Yes / No(※A “current regular smoker” is a person who has smoked a total of 100 or morecigarettes or smoked for 6 months or longer and has been smoking for the last 1 month.)Have you gained ≥10 kg since you were 20 years old?
Yes / No
Have you been exercising for 30 minutes or more each at an intensity that causes a slight
Yes / Nosweat, 2 days or more every week, for at least 1 year?
Have you been exercising such as walking or equivalent more than 1 hour everyday in
Yes / No
your daily life?
Do you walk faster than people of your age and sex?
Yes / No
Have you had a weight gain or loss of ≥3 kg over the last year?
Yes / NoHow fast do you eat compared to others?
Faster / Normal / Slower
Do you have an evening meal within 2 hours before going to bed 3 days or more every
Yes / Noweek?
Do you have snacks (nights meals in addition to 3 daily meals) after the evening meal 3
Yes / No
days or more every week?
Do you skip breakfast 3 days or more per week?
Yes / NoHow often do you drink alcohol (sake, shochu [distilled spirits], beer, liquor, etc.)?
Everyday / Sometimes / Rarely(canʼt drink)
How much do you drink a day, in terms of glasses of refined sake? (A glass [180 mL] of① <1 ② ≥1 and <2③ ≥2 and <3 ④ ≥3
refined sake is equivalent to a medium bottle [500 mL] of beer, 80 mL of shochu (alcohol
content 35 percent), a glass [double, 60 mL] of whiskey, and 2 glasses [240 mL] of wine.)
Do you feel refreshed after a night's sleep?
Yes / NoAre you going to start or have you started lifestyle modifications (e.g., increase physical① No plan to improve.
activity, improve dietary habit)?
② Iʼm going to start in the future (e.g.,
within 6 months).
③ Iʼm going to start soon (e.g., in a month),or I have just started some of them.
④ I already started (<6 months ago).
⑤ I already started (≥6 months ago).
Are you willing to have Health Guidance about lifestyle modifications if the opportunity
Yes / No
arises?

J. Natl. Inst. Public Health, 65（4）: 2016

Relationships between medical expenditures and the Specific Health Checkups scheme in Japan

simple questionnaires can be used to predict trends inlong-term medical expenditures, it will be a very costeffective measure for the integration of health delivery
systems that bring together clinicians, managers, funders
and deliverers. The purpose of this study is to investigate
long-term relationships between the questionnaire
responses that form part of the Specific Health Checkups
scheme and outpatient medical expenditures using panel
data analysis, by comparing Specific Health Checkups
data and the claims accumulated during a 5 year period in
Mishima City in Shizuoka Prefecture, Japan.

II. Methods
Targeted population
Our target population comprised of a cumulative total of

43,740 recipients of Specific Health Checkups, representing
14,848 unique individuals, observed between 2008 and2012. The sample population forms part of the 31,072
National Health Insurance beneficiaries (a health insurance
program administered by municipal governments for selfemployed people, workers engaged in agriculture,
forestry and fisheries, workers of small business and aged
younger than 75) in Mishima City (population circa
110,000) in Shizuoka Prefecture, Japan. The beneficiaries’
average age was 65.3± 7.8, of which 60% were female.
Three hundred and fifteen Specific Health Checkups
recipients were excluded from this study, because they
died during the study period. The total number ofrecipients under the Specific Health Checkups scheme
are: 8,384 in the year of 2008; 8,518 in 2009; 8,996 in 2010;
8,997 in 2011, and 8,845 in 2012 (Table 2).

Table 2 Descriptive statistics of the panel data
n (%)
Year
2008
2009
2010
2011
2012
Number of subjects
8,384
8,518
8,996
8,997
8,845
Age＊
65.0 (7.7)
65.2 (7.7)
65.2 (7.9)
65.4 (7.8)
65.6 (7.8)
Sex (Female)
5,075 (61)5,124 (60)5,369 (60)5,357 (60)5,229 (59)
A medicine to lower blood pressure (Q1)2,855 (34)3,046 (36)3,191 (36)3,269 (36)3,290 (37)Insulin injections or a medicine to lower blood glucose (Q2)672 (8)714 (8)761 (9)775 (9)780 (9)A medicine to lower cholesterol (Q3)1,770 (21)2,071 (24)2,335 (26)2,415 (27)2,560 (29)
Stroke history (Q4)443 (5)477 (6)472 (5)455 (5)456 (5)
Heart disease history (Q5)513 (6)480 (6)515 (6)461 (5)444 (5)Chronic renal failure history (Q6)
16 (0)
15 (0)
24 (0)
27 (0)
25 (0)
Anemia history (Q7)950 (11)856(10)905 (10)856 (10)884 (10)
Current regular smoker (Q8)1,146 (14)1,120 (13)1,158 (13)1,061 (12)1,040 (12)Gained ≥10kg since 20 years old (Q9)2,695 (32)2,650 (31)2,812 (31)2,785 (31)2,714 (31)
Exercising for 30 minutes or more, 2 days or more every week (Q10)3,627 (43)3,898 (46)4,279 (48)4,326 (48)4,336 (49)
Walking more than 1hour everyday (Q11)4,254 (51)4,495 (53)4,745 (53)4,795 (53)4,724 (53)Walk faster than people of your age and sex (Q12)4,508 (54)4,560 (53)4,764 (53)4,892 (54)4,783 (54)Weight gain or loss of ≥3kg over the last year (Q13)
1,682 (20)
1,585 (19)1,681 (19)1,693 (19)1,661 (19)Eating pace (Q14)
Normal
5,751 (69)5,827 (68)6,171 (69)
6,193 (69)6,118 (69)
Faster
1,968 (24)2,020 (24)2,145 (24)2,117 (24)2,044 (23)
Slower664 (8)671 (8)680 (8)687 (8)682 (8)
Evening meal within 2 hours before going to bed (Q15)
1,012 (12)1,072 (13)1,084 (12)1,002 (11)1,029 (12)Have snack after the evening meal (Q16)870 (10)894 (11)935 (10)925 (10)836 (10)Skip breakfast 3 days or more per week (Q17)573 (7)591 (7)617 (7)591 (7)587 (7)
Drink alcohol (Q18) Rarely(can't drink)
4,811 (57)
4,798 (56)5,048 (56)4,967 (55)4,883 (55)
Sometimes
1,873 (22)1,937 (23)2,042 (23)2,119 (24)2,026 (23)Everyday
1,700 (20)
1,783 (21)1,906 (21)1,911 (21)1,935 (22)Feel refreshed after a nightʼs sleep (Q20)
6,488 (77)6,760 (79)
7,016 (78)7,072 (79)7,037 (80)Start lifestyle modifications (Q21)
no plan to improve2,593 (31)2,551 (30)
2,783 (31)2,649 (30)2,660 (30)going to start in the future (within 6 months)
2,507 (30)2,118 (25)2,251 (25)2,241 (25)
2,133 (24)going to start soon ( in a month)
921 (11)
1,294 (15)1,259 (14)1,386 (15)1,242 (14)already started (<6 months ago)
676 (8)743 (9)774 (9)726 (8)780 (9)already started (≥6 months ago)
1,687 (20)
1,812 (21)1,928 (21)1,995 (22)2,029 (23)Willing to have Health Guidance (Q22)4,466 (53)3,990 (47)4,048 (45)3,950 (44)3,652 (41)Outpatient Medical Expenditures＊
13,260 (9,456) 13,604 (9,936) 13,496 (9,678) 13,205 (9,449) 13,307 (9,521)
District District A
2,587
2,574
2,709
2,684
2,625
District B
2,068
2,164
2,227
2,241
2,206
District C
1,772
1,828
1,959
1,939
1,888
District D
1,957
1,952
2,101
2,133
2,126
Health Guidance
Current year
206
198
154
136
135
1 year ago
0
153
169
130
135
2 years ago
0
0
128
145
116
3 years ago
0
0
0
108
121
4 years ago
0
0
0
0
88
＊Data are expressed as means (standard deviation) for ages (years), and means (median) for outpatient medical expenditures (points).

J. Natl. Inst. Public Health, 65（4）: 201677

Yoshimune HIRATSUKA, Yoh TAMAKI, Etsuji OKAMOTO, Hiroe TSUBAKI, Toshiro KUMAKAWA

Statistics and Analyses
We calculated the annual outpatient medical expenditures
for the target individuals between 2008 and 2012 byadding up each year’s claim points. Using official medical
fee tariffs in Japan, 1 point represents 10 Japanese yen.
We combined this data with 5-year results gathered fromthe Specific Health Checkups questionnaires to obtain
longitudinal panel data for each individual, year by year
(Table 2).
In order to narrow down relationships between thequestionnaire responses and outpatient medical expenditures,
we conducted advance analysis. For simplicity’s sake,here we analyzed data in a cross-sectional manner in theadvance analysis without giving consideration to serial
correlation of outpatient medical expenditures over the 5year period. Since outpatient medical expenditures,
defined as dependent variables, included zero and were
skewed, we first adjusted these using logarithmictransformations (log10(outpatient medical expenditure +250), adding the positive value to make the results morenormally distributed. Next, we conducted decision tree
analysis (Classification And Regression Tree analysis :CART) [10], using log-transformed outpatient medical
expenditure as dependent variables, we defined sex, age
and 21 questionnaire items (item Q19 was omitted sinceboth items Q18 and Q19 in Table 1 related to drinking) as
explanatory variables, and thus identified variables that
contributed to predicting outpatient medical expenditures.
Applying CART advance analysis, 15 explanatory variables
were selected from: sex, age, and 21 questionnaire items
for the panel data analysis, on the basis that they
demonstrated greater possibilities of reducing prediction
errors associated with outpatient medical expenditures(Table 3).
Long-term relationships between outpatient medical
expenditures and the questionnaire responses concerning
lifestyle for the Specific Health Checkups accumulated
during the 5 year period, were investigated through theuse of panel data analysis. Panel data analysis is astatistical method, widely used in econometrics and social
science, dealing with panel data (also known as
longitudinal or cross-sectional time-series data) in which
the behavior of entities, such as individuals, organizations,and countries are observed across time. The data is
usually collected over time for the same individuals andthen a regression is conducted across these twodimensions. Panel data analysis has two independent
approaches: the random-effects models, and the fixedeffects models. An advantage of the random-effects modelis that we can include time invariant variables (e.g. sex).
In the fixed-effects model these variables are absorbed by
the intercept. Therefore, if the key explanatory variable isconstant over time, we cannot apply fixed-effects models
to estimate effects on a dependent variable [11]. In thisstudy, we used a random-effect model to identify the
influences by factors that did not change chronologicallyon each individual’s level, such as sex and district.
Random-effect models can be expressed by the following:Yit = bXit + a + uit + fit
Where: i and t are indices for individuals and time, Y isthe dependent variable for i and t, X represents one
independent variable for i and t, and, a and b arecoefficients. uit is the between-entity error, and fit is thewithin-entity error which is also assumed to varystochastically over i or/and t and requires special
treatment of the error variance matrix.
We then conducted a panel data analysis of outpatient
medical expenditures as dependent variables using highly
predictive factors as explanatory variables. On thisoccasion, we added squares of ages as explanatory
variables in order to validate the assumption that anincrease in outpatient medical expenditures accelerated
with aging. As explanatory variables, we also forcibly
applied annual dummy variables to remove the influenceof factors such as revisions of official medical fee tariffs onannual medical expenditures, as well as a further set ofdummy variables representing “districts” in order toTable 3 15 selected variables through the advance analysis by CART
Selected variables
A medicine to lower blood pressure (Q1)
A medicine to lower cholesterol (Q3)
Age
Insulin injections or a medicine to lower blood glucose (Q2)
Stroke history (Q4)Sex
Exercising for 30 minutes or more, 2 days or more every week (Q10)
Walking more than 1hour everyday (Q11)Heart disease history (Q5)
Walk faster than people of your age and sex (Q12)Start lifestyle modifications (Q21)
Feel refreshed after a night's sleep (Q20)
Drink alcohol (Q18)
Weight gain or loss of ≥3kg over the last year (Q13)
Skip breakfast 3 days or more per week (Q17)78

J. Natl. Inst. Public Health, 65（4）: 2016

Variable importance
34
21
16
10
2
2
2
2
2
1
1
1
1
1
1

Relationships between medical expenditures and the Specific Health Checkups scheme in Japan

investigate district-by-district variations among the four
districts in Mishima City. Further, in order to identify the
influences of having received Specific Health Guidance(assistance to improve lifestyle such as diet and exercise
habits according to lifestyle disease risks, hereinafterreferred to as the “Health Guidance”), guidance states forthe current year and guidance histories of the previous 1
to 4 years were applied as lagging variables. We defined
those patients who received actual Health Guidance (i.e.
intermediate guidance and/or aggressive guidance) as
those recipients who received Health Guidance.
Intermediate guidance represents a single counselingsession with health professionals whereas aggressive
guidance represents further series of counseling for morethan three months. We used R and Stata MP12 for the
analyses and set a significance level at 5%.
Ethical consideration
This study was approved by the National Institute of

Public Health (NIPH-IBRA#12061).

III. Results
The total annual number of checkups undertaken by
the recipients ranged approximately from 8,400 to 9,000.
The average age of the patients was approximately 65
years, of which approximately 60% were female across all
sample periods (Table 2).
With regard to all questionnaire items, the following
two: “taking medicine to lower cholesterol” (Q 3 ), and“exercising for 30 minutes or more, 2 days or more every
week (exercise)” (Q10: exercise), showed an increasing
trend every year. On the other hand, the number of:current regular smokers (Q8); people who rarely drink(Q18), and people who are willing to receive Health
Guidance (Q22) showed a declining trend every year
(Table 2).

Table 4 Results of the longitudinal relationship between questionnaire items and outpatient medical expenditures using
panel data analysis (n=14,848, 43,740 records)
Questionnaire items

Answer choices

Age
years
Squares of ages
Sex
(Female/Male)A medicine to lower blood pressure (Q1)
(Yes/No)
Insulin injections or a medicine to lower blood glucose (Q2)
(Yes/No)A medicine to lower cholesterol (Q3)
(Yes/No)
Stroke history (Q4)
(Yes/No)Heart disease history (Q5)
(Yes/No)
Exercising for 30 minutes or more, 2 days or more every week (Q10) (Yes/No)
Walking more than 1hour everyday (Q11)(Yes/No)Walk faster than people of your age and sex (Q12)(Yes/No)
Weight gain or loss of ≥3kg over the last year (Q13)(Yes/No)
Skip breakfast 3 days or more per week (Q17)(Yes/No)
Drink alcohol (Q18)
Rarely (canʼt drink)
Sometimes
EverydayFeel refreshed after a nightʼs sleep (Q20)(Yes/No)
Start lifestyle modifications (Q21)
no plan to improve
Within 6 months
In a month
Already started(< 6 months ago)
Already started(≥6 months ago)
Year
2008
2009
2010
2011
2012
District
District A
District B
District C
District D
Health Guidance
Current year(Yes/No)
1 year ago(Yes/No)
2 years ago(Yes/No)
3 years ago(Yes/No)
4 years ago(Yes/No)_cons
CI, confidence interval

coefficient
−0.01
0.00
0.05
0.23
0.20
0.13
0.09
0.04
−0.02
−0.02
−0.03
0.03
−0.01
Reference
−0.02
−0.05
−0.03
Reference
0.02
0.03

Panel data analysis
P value
95% CI
0.120−0.016 − 0.002<0.0010.000 − 0.000<0.0010.032 − 0.063<0.0010.220 − 0.246<0.0010.175 − 0.215<0.0010.122 − 0.147<0.001
0.063 − 0.107
0.001
0.016 − 0.059
0.001−0.025 − −0.006<0.001−0.024 − −0.007<0.001−0.043 − −0.025<0.0010.017 − 0.036
0.151−0.032 − 0.005<0.001<0.001<0.001−0.035 − −0.011−0.064 − −0.032−0.038 − −0.018<0.001<0.0010.011 − 0.032
0.014 − 0.038

0.04<0.0010.023 − 0.050

0.03
Reference
0.01
0.00
−0.01
−0.02
Reference
0.00
0.03
0.01
−0.06
−0.04
0.02
−0.01
0.03
3.30

<0.0010.018 − 0.040

0.104
0.595
0.008
<0.001−0.002 − 0.017−0.007 − 0.012−0.023 − −0.004−0.030 − −0.009

0.996
0.012
0.597
<0.001
0.015
0.184
0.531
0.339
<0.001−0.019 − 0.019
0.005 − 0.044−0.014 − 0.024−0.088 − −0.032−0.069 − −0.007−0.012 − 0.060
0.059 − 0.030−0.036 − 0.103
3.04 − 3.56

J. Natl. Inst. Public Health, 65（4）: 201679

Yoshimune HIRATSUKA, Yoh TAMAKI, Etsuji OKAMOTO, Hiroe TSUBAKI, Toshiro KUMAKAWA

Annual average outpatient medical expenditures ranged
between 13,205 and 13,604 points, with medians between
9,449 and 9,936 points. There were no large annual
variations in the number of health-checkup recipients atthe district level either. The number of Health Guidance
recipients showed a decreasing trend every year, from 206
individuals in 2008 to 135 individuals in 2012 (Table 2).
Table 4 displays the results of the panel data analysis(random-effect model, p<0.001, Breusch-Pagan Lagrange
multiplier test). There was a significant, nonlinear
increase in outpatient medical expenditures associatedwith advancing age, as indicated by the significant agesquared terms (Table 4).
The following questionnaire items: sex (female);
medication (medicine to lower blood pressure (Q1);
insulin injections or medicine to lower blood glucose (Q2);medicine to lower cholesterol level (Q3)); stroke history(Q4); heart disease history (Q5), and “weight gain or lossof ≥3kg over the last year” (Q13: weight change), eachdisplayed significant positive coefficients. Medication had
the greatest influence on outpatient medical expenditures,with the highest coefficient being 0.23 for antihypertensive
agents which showed an influence 1.7 times greater thanhypolipidemic agents and 1.2 times greater thanhypoglycemic agents.
With regard to the issue of starting lifestylemodifications (Q21), all groups (e.g. going to start in the
future, going to start soon, etc.) showed significant
positive coefficients in comparison to the “no plan to
improve” group.
Meanwhile, “exercise” (Q10), “walking” (Q11), “walkfast” (Q12), and “good sleep” (Q20) showed significant
negative coefficients. In particular the “walk fast” (Q12)and “good sleep” (Q20) items showed lower coefficientthan the others.
With respect to drinking frequency (Q18), a comparison
between a group that “rarely drink”, with groups that“drink sometimes” or “every day”, showed significant
negative coefficients. Results for 2011 and 2012 showedsignificant negative coefficients in comparison to 2008.
With regard to districts, only district C showed asignificant positive coefficient compared to district A.
Health Guidance in a given year and a year earlier
showed significant negative coefficients, however Health
Guidance given 2 years before, or earlier, showed nosignificant relationships.

IV. Discussion
This study investigated relationships between outpatient
medical expenditures and lifestyle disease risks identified
by the questionnaires, based on large-scale data comprisingof the total Specific Health Checkups data and the claimsof one local government accumulated over 5 years that80

covered about 44,000 people on a cumulative basis. This
study indicated that questionnaire responses relating to
the Specific Health Checkups scheme were a useful
information source and as a possible predictor of trends inlong-term outpatient medical expenditures. It was natural
to assume that medication and disease histories would
strongly influence medical expenditures, however it wasalso found that physical activities, fast walking, and good
sleep independently related to a reduction of medical
expenditures. Moreover, Health Guidance was also seen
to have an association with the reduction of medical
expenditures for the same year that such guidance wasgiven, as well as in the following year.
O u r p r e v i o u s s t u d y [ 3] i d e n t i f i e d s i g n i f i c a n t
relationships between: a) stroke, heart disease histories,“walking” (Q11) and an increase in medical expendituresand b) between females, “walk fast” (Q12), “good sleep”(Q20) and a decrease in medical expenditures, observed 4
years after the Specific Health Checkups and notably at
the 80% quantile, where a large increase was observed.
However, our previous study investigated relationships
between medical expenditures (inpatient and outpatient)
on two distinct occasions i.e. the year of the Specific
Health Checkups (2008) and 4 years later (2012), andreferencing the questionnaire responses collected in 2008.
Consequently, chronological changes in medical
expenditures as well as questionnaire responses for theyears between 2008 and 2012 were not investigated.
Furthermore, the individuals who received Health Guidancewere not included. In spite of those inconsistencies, both
our present and previous studies indicated that “walk fast”(Q12) and “good sleep” (Q20) as lifestyle items that relate
to a reduction in medical expenditures.
The most influential factor on increased outpatient
medical expenditures turned out to be medication, i.e.
medicine to lower blood pressure (Q1), insulin injections
or medicine to lower blood glucose (Q2) and medicine tolower cholesterol level (Q3). The average expenditures
for antihypertensive agents, hyperlipidemia agents andantidiabetic agents per prescription in Japan in 2013 were683, 357, and 340 Japanese yen, respectively [12]. The
results of the current study showed that outpatient
medical expenditures, represented by examination costs
and treatment costs, were also higher for patients who
took antihypertensive agents as compared to thosepatients who took antidiabetic or hyperlipidemia agents.
Stroke history that had hypertension as an underlying
disease also showed relatively high positive coefficients.
“Weight change” (Q13) exhibited a trend to increase
outpatient medical expenditures. There is a report
indicating that weight gain (more than 2 kg) is strongly
related to an increased risk of hypertension andhypercholesterolemia after adjustments for possible
confounding factors [13] while the current study goes

J. Natl. Inst. Public Health, 65（4）: 2016

Relationships between medical expenditures and the Specific Health Checkups scheme in Japanfurther to suggest that weight change (gain or loss of≥ 3 kg) may have led to the advent of these diseases andinfluenced medical expenditures.
Two physical activity items, “exercise” (Q10) and“walking” (Q11), both showed significant negative
coefficients. This result indicates that those patients withdaily exercise habits paid approximately 5 % lower
medical expenditures. In particular the “walk fast” (Q12)
item exhibited a lower coefficient than the other factors. It
is a widely known that exercise has a positive influence on
health [14], however our study has identified a similar
influence on medical expenditures.
Non-restorative sleep, which includes short sleep
duration and poor sleep quality, is also associated withlifestyle factors [15]. Wakasugi et al’s cross-sectional
study using Japanese Specific Health Checkups data in2008 [16] reported that unhealthy lifestyle factors wereassociated with non-restorative sleep, after adjustment
for possible confounders such as hypertension,
hypercholesterolemia, diabetes, and chronic kidneydiseases. In this current study, a significant relationshipwas identified between “good sleep” (Q20) and reductionof medical expenditures, after adjustment for possible
confounders (Table 4).
Regarding drinking of alcohol (Q18), it turned out thatthe groups that “sometimes drink” and “drink every day”
incurred lower outpatient medical expenditures than thegroup that rarely drink alcohol. The results this time
showed that coefficients of the “drink every day” groupwere higher than the ones of the “sometimes drink”
group. Preventive effects of moderate drinking of alcoholon many diseases have been reported [17, 18], however
the result might also be indicating that those patients who
were healthy enough to drink every day consequentially
incurred less medical expenditures. On the issue ofstarting lifestyle modifications (Q21), the groups thatwere “going to start,” have “already started,” etc., showed
positive links with an increase in medical expenditures,compared to the group that did not have plans toundertake lifestyle improvements. This might indicatethat many patients who were not healthy and needed to
go to hospitals worked on lifestyle modifications. From
this, we also cannot deny the possibility of the existenceof an opposite causal relationships linked between medical
expenditures and drinking and lifestyle modifications.
When taking account of medical expenditures, a seriesof revisions of official medical fee tariffs implemented
every 2 years changed medical data reference points
requiring further analysis in order to exclude the effect ofthat influence. For the purposes of this study, weinvestigated each individual year’s results by applying
annual “dummy” variables and in so doing we observed a
reduction in medical expenditures in 2011 and 2012
compared to 2008. Between these years, revisions ofofficial medical fee tariffs were implemented in 2010 and2012 in Japan. In both years, the policy focus was onenhancing acute medical care mainly throughhospitalization [19, 20], and it is possible that an influence
from such a policy was reflected.
Comparing district characteristics, district C showed asignificant increase in medical expenditures comparedwith district A. This indicated that local variation existed
even on a city level. This also suggests that implementationof medical service delivery systems should also consider
local characteristics, even within a city.
A report by Mitsutake et al [21] discusses therelationship between the Health Guidance and medical
expenditures in a study of relationships between
outpatient medical expenditures and Health Guidance for
1 year commencing in 2008. After adjusting differencesamong medical insurers, age, sex, and the presence of
medication, a significant relationship was identified
between the group that received intermediate guidance
and a reduction in medical expenditures. On the other
hand, no relationships were identified with aggressive
guidance. Okamoto [22] conducted a propensity scoreanalysis to investigate a causal relationship between
Health Guidance (both intermediate and aggressive),
metabolic syndrome-related outpatients and pharmaceuticalexpenditures during the three year period following theinitial Health Guidance. Although no difference wasobserved between the health guidance group and thematched control group, regression analysis suggested asmall but no significant reduction in health careexpenditures. Our study covered five years and included
adjustments for patients' lifestyles, as well as data relating
to their ages, sex, and medication histories. As a result, it
was found that Health Guidance, including both intermediate
guidance and aggressive guidance, was related to a
reduction in medical expenditures in the same year thatthe guidance was given, as well as the following year,
however Health Guidance received two or more years
earlier did not exhibit a relationship with medical
expenditures. The size of such effects was interpreted tobe approximately a 15% reduction in medical expendituresin the same year and approximately 10% reduction in thefollowing year. It was also found that Health Guidance had
three times more influence than “exercise” (Q10) or“walking” (Q11) in the same year, and had twice as muchinfluence as “exercise” (Q10) or “walking” (Q11) in thefollowing year. Health Guidance (aggressive guidance)
was reported to significantly reduce the values of weight,
body mass index (BMI), abdominal circumference,
hemoglobin A1c and neutral fat, and to significantly
increase the values of HDL cholesterol 1 year following
the guidance, even after the influences of backgroundfactors were adjusted for [23]. It was suggested that thesechanges were possible causes of medical expenditure

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Yoshimune HIRATSUKA, Yoh TAMAKI, Etsuji OKAMOTO, Hiroe TSUBAKI, Toshiro KUMAKAWA

reductions.
It is widely accepted that smoking is closely associatedwith morbidity and mortality from lifestyle diseases [24,25], and therefore, smoking has a major impact on both
health status and medical expenditures. However, a reporton the relationship between smoking and medical
expenditures in Japan by Izumi et al. showed that while
male smokers incurred 11% more medical costs compared
to “never smokers”, however costs remain almost thesame for females (after adjustment for age, physicalfunctioning status, alcohol consumption, BMI, andaverage time spent walking) [26]. In our study, smoking
was excluded from the explanatory variables in the panel
data analysis by applying CART advance analysis. Thismay be because about 60% of the samples are female inour study.
We acknowledge that the current study has certain
limitations. Firstly, the study analyzed only outpatient
medical expenditures and did not include inpatient
medical expenditures, expenditures on medicines
dispensed at pharmacies, etc. The general proportions of
outpatient medical, inpatient medical, pharmacy anddental treatment expenditures are approximately 10:11:5:2
in Japan, so outpatient medical expenditures alone cannot
rightly represent overall medical expenditures. However,inpatient medical expenditures did not seem to be an
appropriate analysis subject to investigate relationships
using the questionnaire. Inpatient medical expenditures
had strong influences from diseases and conditions thatnecessitated hospitalization, rather than from daily
lifestyle items represented by the questionnaire responses
we reviewed. On the other hand, outpatient medical
expenditures were expected to have stronger relationships
with lifestyle because, to a large extent, they involved dayto-day outpatient medical expenditures. Secondly, thestudy only investigated beneficiaries insured by amunicipal National Health Insurance program and did notcover the entire population. However, the study was aninvestigation from the standpoint of a local government as
a National Health Insurance insurer, and useful information
to formulate future medical policies at a local level was
obtained. In addition, the study reports the results of theinvestigation in Mishima City only, therefore any
generalizations drawn from of these results must be
examined cautiously. The fact that significant variation in
medical expenditure was found on a district level within
one city seems to call for investigations on subdivided
levels rather than across all of Japan or at the prefecturelevel. In relation to the loss of follow up subjects, while we
did not consider subjects relocating out of Mishima City
between 2008 to 2012, we did however excluded 315
subjects who died during this period. The data relating todeaths was not derived from health insurance claims but
rather from a list of fatalities provided by Mishima City,

82

therefore, the subjects that moved out of Mishima City
during this period may not necessarily be included in thelist. Finally, lifestyles and medical expenditures are notconnected by direct causal relationships. There are many
diseases other than lifestyle diseases that may be causedby the influence of lifestyle. Medical expenditures arereflections of all those diseases and many other factors,
other than lifestyle, also have influences on them. We will
follow up this study with more detailed investigations
using results of the Specific Health Checkups scheme,
diagnoses recorded in claims and details of medical
services provided.

V.

Conclusion

Low-cost and easily executable questionnaires under
the Specific Health Checkups scheme enable not only risk
assessment of target individuals but also prediction of
long-term trends in outpatient medical expenditures.
Physical activities, fast walking, and good sleep displayed
relationships with reductions in outpatient medical
expenditures. Furthermore, Health Guidance can also be
associated with lowering outpatient medical expendituresin the same year as the giving of that guidance, as well as
in the following year.

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J. Natl. Inst. Public Health, 65（4）: 201683

Yoshimune HIRATSUKA, Yoh TAMAKI, Etsuji OKAMOTO, Hiroe TSUBAKI, Toshiro KUMAKAWA

特定健康診査データと医療費の関連
─特定健康診査における質問票と外来医療費の関連についての検討─
平塚義宗 1 ，2 ），玉置洋 1 ），岡本悦司 3 ），椿広計 4 ），熊川寿郎 1 ）国立保健医療科学院医療福祉サービス研究部
順天堂大学眼科学教室
3 ）福知山公立大学
4 ）独立行政法人統計センター
1）
2）

抄録
背景：特定健康診査及び特定保健指導が2008年に実施されてから 7 年が経過した．現在，地方自治体
レベルにおいては特定健康診査と医療レセプトのデータを突合することで，医療費に対する長期的な関連因子の検討が可能となっている．特定健康診査における生活習慣病のリスクに関する質問票と外来医療費の長期的な関連について検討を行う．
方法：対象は静岡県三島市における2008年から2012年の 5 年間の特定健康診査受診者（延べ43,740人，14,848個人）（平均65.3±7.8 歳，女性60％）． 5 年間に累積された特定健康診査における質問票への回
答と医療レセプトから得られた外来医療費の長期的な関連について縦断パネルデータ分析を用いて検討した．
結果：薬剤の使用（血圧，血糖，コレステロール降下薬）と脳卒中・心臓病の既往歴及び体重変化（ 3
kg以上の増加または減少）は外来医療費の増加と関連していた．一方，運動/身体活動，速い歩行，十分な睡眠は外来医療費の減少と関連していた．特定保健指導は実施年とその翌年において外来医療費の減少と関連していた．
結論：低費用で簡易的に実施可能な特定健康診査の質問票は，生活習慣病に対するリスク評価だけでなく，長期的な外来医療費の変化予測に役立つ可能性がある．運動/身体活動，速い歩行，十分な睡眠，および特定保健指導には医療費減少と関連があること示唆された．
キーワード：医療費，特定健診，特定保健指導，身体活動

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J. Natl. Inst. Public Health, 65（4）: 2016