Journal of the National Institute of Public Health2013 Vol.62 No.1 p.13−30

Topics：Strategic management of evidence-based health and medical care policy: How to use new Digital Big Data in health care system＜Review＞
Evaluation of the health check up and guidance program
through linkage with health insurance claims
Etsuji OKAMOTO, Yoshimune HIRATSUKA, Koichi OTSUBO, Toshiro KUMAKAWA
National Institute of Public Health, Department of Health & Welfare Service Research
Abstract
Objectives: To evaluate the health check up and guidance program of Mishima City’s National Health
Insurance program by analyzing how health risks, lifestyles and health guidance are related with the
cumulative incidence and outpatient medical charges of metabolic syndrome.
Methods: The health check up and guidance data provided in fiscal 2008 were linked with healthinsurance claims submitted between June 2008 and October 2012 on an individual basis. Items evaluatedwere (1) health risks detected by health checks, (2) lifestyles factors obtained from questionnaires and(3) health guidance provided to recipients at risk for metabolic syndrome. Outcomes were defined as
the incidence and outpatient medical charges related to metabolic syndrome. Comparison was
performed between groups categorized by health risks, lifestyles and reception of health guidance using
the outcomes accumulated for 53 months since June 2008.
Results: Health risks and reception of health guidance were good predictors of cumulative incidence
and outpatient medical charges for metabolic syndrome. However, lifestyles factors obtained fromquestionnaires were not clearly related with expected outcomes. For smoking and drinking, theoutcomes were opposite expectations: smokers and drinkers showed lower incidence and outpatient
medical charges related to metabolic syndrome.
Discussion: Counter-intuitive results were found by linking health check up and guidance data withhealth insurance claims. Evaluation of health check ups and guidance should be analyzed by outcomes
obtained from health insurance claims.
keywords: metabolic syndrome, health check up and guidance, health insurance claims, lifestyle,proportional distribution method (PDM)(accepted for publication, 30th January 2013)

I.

Introduction

After five years of the first health care cost containment
plans (HCCCP) has elapsed, evaluation of the health checkup and guidance (HC&G) program targeting metabolicrelated syndromes will be conducted in fiscal 2013,
pursuant to the requirement of the Elderly Health Care
Security Act (EHCSA) [1]. Since the HC&G program isintended to control health care expenditure in the medium-long term, it is necessary for health insurers to evaluate
how the HC&G program affected the utilization of healthcare services as well as the incidence of metabolic-relateddiseases. To achieve these goals, it is necessary to link the
HC&G data to health insurance claims (HIC) data on an
individual basis. Online submission of HICs has developed
dramatically during the last five years and HICs provide
detailed information on patients’ diagnoses, medications
and utilization [2]. However, such linkage of HC&G dataand HICs data has not been routinely conducted because of

Corresponding author: Etsuji Okamoto
2-3-6, Minami, Wako-shi, Saitama, 351-0197, Japan.
Fax: +81-48-468-7985
E-mail: atoz@niph.go.jp

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Etsuji OKAMOTO, Yoshimune HIRATSUKA, Koichi OTSUBO, Toshiro KUMAKAWA

2．Outline of Mishima City’s HC&G program
HC&G is provided to beneficiaries aged 40 to 74 by the
insurers pursuant to the national uniform requirements set
forth by the EHCSA. Recipients of health check ups werecategorized into (1) normal (information provision only), (2)motivational intervention and (3) aggressive intervention
pursuant to the national standard [3]. Those categorized
into (2) or (3) are considered to be at risk of metabolic
syndrome and were invited for health guidance. However,those aged 65 and over will not be categorized for“aggressive intervention” and all those identified to be atrisk of metabolic syndrome will be categorized for“motivational intervention”. Also, those who answered thatthey are taking any medications for diabetes, hypertension
or hyperlipidemia were also excluded from health guidance [4].
A total of 12,304 beneficiaries received health check ups
for metabolic syndrome in the fiscal 2008. Of them, 8,616(70.4%) answered a questionnaire and 3,344 (27.2%) wereaged 65 and over in 2008. The number of recipients who
were categorized for “motivational” and “aggressive”
intervention was 694 and 249, respectively (166 and 249,
respectively for those aged 65 or less). The small number of
those categorized for “motivational” and “aggressive”
intervention may be explained by a considerable number of
those already under medical treatment (N=1554) and an
inability to categorize (N=5733) because of insufficient
preparation in the first year.

In this phase, selected items (diagnoses, date ofdiagnosis, number of days per claim, charges in points in aclaim, flag of primary diagnoses, flag of rule-out diagnoses,etc) were input from all claims, including those submitted
in paper form. Online submission was not fully achieved inand around 2008 but there were no biases from theomission of computerized claims because all claims
including paper forms were included. Although the data did
not include detailed items such as medication or clinicalprocedures, they recorded all diagnoses and the weresuitable for estimation of disease-specific charges using
proportional distribution method (PDM) [5].
(b) Computerized data for 12 months from November 2011
to October 2012
In this phase, the entire contents of computerized HIC
data were available. The same items as in (a) wereextracted and the two phases were combined asconsecutive data covering 53 months.
Disease-specific charges were estimated by the first twodigits of the ICD10. “Metabolic-related diseases” were
defined as E1 (diabetes), E7 (hyperlipidemia) and I1(hypertension). Estimation of disease-specific charges was
conducted using PDM after excluding rule-out diagnoses(rule-out diagnoses were included only when ALL of thediagnoses in a claim were rule-out diagnoses). Charges
were obtained from the number of points (at =10 yen perpoint) claimed in HICs.
Cumulative medical charges were counted by calendar
month. Cumulative incidence of a disease was counted bydate of diagnosis because each diagnosis in a HIC contains
the date of diagnosis. The cumulative incidence wascounted by the first date of diagnosis after June 2008 forevery recipient of a health check up in fiscal 2008. If a
recipient of a health check up on July 10, 2008 visits clinic A
on July 20 and was then referred to hospital B on 30th July,
one case is counted on the 20th day since June 2008 and no
more counts will be added even if the recipient visited more
hospitals or clinics later on.
Also, it should be noted that the date of diagnosis in a
HIC is the date when the hospital or clinic started thetreatment, and it may or may not be the date when thepatient was first diagnosed as having the disease. The
cumulative incidence and medical charges evaluated in thisstudy are the accumulation of n˙ e˙ w˙ l˙ y˙ diagnosed metabolicrelated diseases after June 2008 and may overestimate thereal incidence and medical charges of disease detected byhealth check ups.

3．Health insurance claims
HIC data were obtained from Mishima City’s NHI
program and HCSLE. The contents of the data is divided
into the following two phases.
(a) Abbreviated data for 41 months from June 2008 to
October 2011

4．Modeling of evaluation and outcome measures
Evaluation was performed in three dimensions: (a) how
health risks detected in health checks are related withthe outcome, (b) how lifestyle factors obtained fromquestionnaires is related with the outcome and (c) how
health guidance affects the outcome. Figure 1 illustrates

technical difficulties and complexities of HICs data.
The authors evaluated the outcome of the HC&G
program of Mishima City in Shizuoka prefecture, by linking
the HC&G data and HIC data on an individual basis andreport on the relationship between the HC&G program andthe incidence and outpatient medical charges related tometabolic syndrome obtained from HICs data.

II. Data and methods
1．Outline of Mishima City’s NHI program
Mishima city had a population of 111,773 (46,304
households) at the end of fiscal 2010, of whom 32,310(18,431 households) were insured by the City’s NHI
program (39.6% of households). Of the NHI beneficiaries, a
total of 22,948 were aged 40 to 74, the target age for HC&G.
The number of elderly people aged over age 75 who were
insured by the Health Care System for the Late Elderly(HCSLE) was 11,612 as of March 2012.

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Evaluation of the health check up and guidance program through linkage with health insurance claims

which portion of the health check up data in fiscal 2008 wasused for each dimension.
Outcome was measured in two dimensions: (a) incidenceof newly diagnosed metabolic related diseases and (b)outpatient medical charges of both all diseases andmetabolic related diseases. Both outcomes were measured
by cumulative incidence and charges for 53 months startingfrom June 2008. Inpatient claims, as well as DPC andpharmacy claims were not included in the outcome
measures. Inclusion of inpatient claims may bias theoutcome because a small number of claims may involve
high charges and are not theoretically appropriate for
evaluation of primary prevention [6]. Pharmacy claims
were not included in the first phase of claims data (June2008-October 2011), making the continuous comparison
impossible.
(a) Health risks evaluated:(i) Blood pressure:
Comparison was made among three groups: normal(systolic blood pressure (SBP)<=129mmHg AND diastolicblood pressure (DBP)<=84mmHg), in need of guidance(SBP=130-139mmHg or DBP=85-89mmHg) and in need oftreatment (SBP>=140mmHg or DBP>=90mmHg). Outcome
was measured by cumulative incidence and outpatient
medical charges for hypertension (code I1 in ICD10).
(ii) LDL cholesterol:
Comparison was made among three groups: normal(LDL<120mg/dl), in need of guidance (LDL=120-139mg)
and in need of treatment (LDL>=140mg). Outcome wasmeasured by cumulative incidence and outpatient medical
charges of hyperlipidemia (code E7 in ICD10).
(iii) HbA1c:
Comparison was made among three groups: normal(HbA1c<5.1%), in need of guidance (HbA1c:5.2-6.0%) and inneed of treatment (HbA1c>=6.1%). Outcome was measured
by cumulative incidence and outpatient medical charges ofdiabetes (code E1 in ICD10).
(iv) creatinine:
Comparison was made between two groups: normal(creatinine<1.04mg/dl for men, 0.79mg/dl for women), in
need of guidance or treatment (creatinine>=1.04mg/dl formen, 0.79mg/dl for women). Outcome was measured bycumulative incidence and outpatient medical charges forrenal failure (code N1 for ICD10).
(b) Lifestyle foctors evaluated:(i) Smoking:Comparison was made between smokers and non-smokers.
(ii) Exercise:Comparison was made among three groups: active(exercising for 30 minutes/day at least two days/week forat least one year), moderate (walking for at least one
hour/day) and no exercise.
(iii) eating fast:Comparison was made among three groups: fast eaters,
normal eaters and slow eaters.
(iv) eating late at night:
Comparison was made between those who eat dinner
within two hours of going to bed three times or more per
week and those who act otherwise.
(v) eating snacks:
Comparison was made between those who eat snacks after
dinner for three or more times per week and those who act
otherwise.
(vi) skipping breakfast
Comparison was made between those who skip breakfasts
three or more times per week and those who act otherwise.
(vii) Drinking:Comparison was made among three groups: heavy drinkers(drinking everyday),moderate
drinkers(drinkingoccasionally) and non-drinkers.
Outcome was measured by cumulative incidence
and outpatient medical charges for all metabolic relateddiseases (code E1, E7 and I1 in ICD10:).
(c) Evaluation of health guidance:
Comparison was made between those who received
health guidance and those who did not among those werecategorized into “motivational” or “aggressive” intervention
in health check in 2008. The comparison was limited to

Figure 1 Structure of health check recipients in FY2008 and the portion used for evaluation

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Etsuji OKAMOTO, Yoshimune HIRATSUKA, Koichi OTSUBO, Toshiro KUMAKAWA

those aged <65 in 2008 (born in 1944 or later) to allow anunbiased comparison. Outcome was measured bycumulative incidence/outpatient medical charges of all
metabolic related diseases (ICD10:E1, E7 and I1).
(d) Ethical considerations
This study was conducted by contract with Mishima cityin accordance with its Privacy Protection Ordinance.
Personal identifiers were coded by the city and released tothe authors under security agreement.

III. Results
1.

Health risks
The results of four risks detected from health check ups(blood pressure, HbA1c, LDL and creatinine) and thecorresponding outcomes (cumulative incidence and medical
outpatient charges) are presented in Figures 2-9. In all four
risk categories, a consistent tendency was observed: thecumulative incidence and outpatient medical charges werehighest in the group in need of treatment, followed by thegroup in need of guidance and lowest in the normal group.
The most conspicuous was with creatinine, a risk factor for
renal failure. Even if the difference in cumulative incidence
between those at risk and normal individuals was narrow, it
brings about a large difference in cumulative charges, as
shown in Figure 9 suggesting high charges for treatment ofrenal failure, such as dialysis.
2.

Lifestyles
The results of seven lifestyle foctors obtained fromquestionnaires and outcomes are presented in Figure 10-23.
As a sharp contrast to health risks, the relationship between
lifestyle and outcomes was not so clear. One might imagine
that those with unhealthy lifestyles are more likely to havethe disease they are at risk for and incur corresponding
medical charges. However, such presumptions do not hold.
As for smoking, smokers showed lower cumulative
incidence and outpatient medical charges than nonsmokers. The difference between them was narrow when
medical charges were limited to metabolic-related diseases
as shown in Figure 11. At least it is not likely that smoking
cession will lead to savings in health care expenditure. The
same holds for drinking. Contrary to expectation, nondrinkers showed the highest cumulative incidence of
metabolic-related diseases among three groups, although
the difference was small for cumulative medical charges formetabolic-related disease.
The difference is less clear for eating habits. Breakfast
skippers showed higher cumulative incidence of metabolicrelated disease particularly after 800 days since June 2008(see fig. 20). Their difference in cumulative outpatient
medical charges consistently widened as months passed by
Figure 21.

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3.

Health guidance
The results of the outcomes of health guidance arepresented in Figure 24-25. The outcomes unequivocallyshow lower cumulative incidence and outpatient medical
charges for those who received health guidance.

IV. Discussion
Five years after the introduction of the HC&G in April
2008, evaluation results of HC&G by linking HC&G datawith HIC data began to be reported. One of the strengths ofthe HC&G program was the standardized health check up
and questionnaire items. These standardized items assure
the comparability among results obtained from different
insured populations.
Suzuki-Saito studied a total of 29,490 recipients of HC&G
in fiscal 2008 with an almost similar analytical modeling tothis study: (1) health risks of three metabolic relateddiseases detected in health checks and (2) lifestyle factors
obtained from the questionnaire [7]. She first compared the
median outpatient medical charges among groups stratified
by body mass index (BMI) and confirmed obesity was
associated with higher median outpatient medical charges.
However, as she further analyzed the relationship withhealth risks and lifestyle factors stratified by BMI,
unexpected outcomes emerged. While health risks weresignificantly associated with medical charges, therelationship with lifestyle factors was either non-significant(exercise and eating) or even negative (smoking anddrinking). Actually, groups of lower medical charges
showed higher prevalence of smoking and drinking in all
BMI stratifications.
Suzuki-Saito’s results were consistent with the results ofthis study. Health risks detected by health check ups
served as good predictors of cumulative incidence andoutpatient medical charges for metabolic related diseases
but lifestyle factors obtained from the questionnaire did not.
Lifestyle factors such as exercise and eating habit were notclearly related with these outcomes. Smokers and drinkers
tend to have lower incidence and outpatient medical
charges for metabolic related diseases. Suzuki-Saito
explains the observed counter-intuitive results by “Thereason for the difference in medical charges cannot beclarified because disease-specific analysis was not possible
in our study. The significantly lower prevalence of drinkers
and smokers in high medical charge groups might be dueto the intervention of hospitals and clinics”. In comparison,
the results of this study have the strength that diseasespecific medical charges were objectively estimated by
PDM. It may be that exercise and eating are not relatedwith incidence and outpatient medical charges, andunhealthy lifestyles such as smoking and drinking are
negatively related with incidence and outpatient medical
charges for metabolic-related diseases.

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Evaluation of the health check up and guidance program through linkage with health insurance claims

Figure 2 CUMULATIVE INCIDENCE of NEWLY DIAGNOSED HYPERTENSION (ICD10:I1)
stratified by BP in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)

Figure 3 CUMULATIVE OUTPATIENT MEDICAL CHARGE percapita for hypertension (ICD10:I1) newly diagnosed after June 2008
stratified by BP in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)

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Etsuji OKAMOTO, Yoshimune HIRATSUKA, Koichi OTSUBO, Toshiro KUMAKAWA

Figure 4 CUMULATIVE INCIDENCE of NEWLY DIAGNOSED DIABETES (ICD10:E1)
stratified by HbA1c in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)

Figure 5 CUMULATIVE OUTPATIENT MEDICAL CHARGE percapita for diabetes (ICD10:E1) newly diagnosed after June 2008
stratified by HbA1c in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)18

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Evaluation of the health check up and guidance program through linkage with health insurance claims

Figure 6 CUMULATIVE INCIDENCE of NEWLY DIAGNOSED HYPERLIPIDEMIA (ICD10:E7)
stratified by LDL in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)

Figure 7 CUMULATIVE OUTPATIENT MEDICAL CHARGE percapita for hyperlipidemia (ICD10:E7) newly diagnosed after June 2008
stratified by LDL in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)

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Etsuji OKAMOTO, Yoshimune HIRATSUKA, Koichi OTSUBO, Toshiro KUMAKAWA

Figure 8 CUMULATIVE INCIDENCE of NEWLY DIAGNOSED RENAL FAILURE (ICD10:N1)
stratified by creatinine in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)

Figure 9 CUMULATIVE OUTPATIENT MEDICAL CHARGE percapita for renal failure (ICD10:N1) newly diagnosed after June 2008
stratified by creatinine in health check in 2008
(both sexes, all ages>=40, rule-out diagnoses excluded)

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Evaluation of the health check up and guidance program through linkage with health insurance claims

Figure 10 CUMULATIVE INCIDENCE of metabolic syndrome (E1, E7, I1)
between smokers and non smokers of recipients of health-check in FY2008
rule-out diagnoses excluded

Figure 11 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between smokers and nonsmokers

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Figure 12 CUMULATIVE INCIDENCE of metabolic syndrome (E1, E7, I1)
between exercisers and non exercisers of recipients of health-check in FY2008
rule-out diagnoses excluded, some of mederate exercisers overlap with nonexercisers

Figure 13 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between exercisers and nonexercisers

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Evaluation of the health check up and guidance program through linkage with health insurance claims

Figure 14 CUMULATIVE INCIDENCE of metabolic syndrome (E1, E7, I1)
between fast eaters and slow eaters of recipients of health-check in FY2008
rule-out diagnoses excluded

Figure 15 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between fast eaters and slow eaters

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Figure 16 CUMULATIVE INCIDENCE of metabolic syndrome (E1, E7, I1)
between late eaters and normal eaters of recipients of health-check in FY2008
rule-out diagnoses excluded

Figure 17 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between late eaters and normal eaters

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Evaluation of the health check up and guidance program through linkage with health insurance claims

Figure 18 CUMULATIVE INCIDENCE of metabolic syndrome (E1, E7, I1)
between snack eaters and non eaters of recipients of health-check in FY2008
rule-out diagnoses excluded

Figure 19 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between snack eaters and non snack eaters

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Etsuji OKAMOTO, Yoshimune HIRATSUKA, Koichi OTSUBO, Toshiro KUMAKAWA

Figure 20 CUMULATIVE INCIDENCE of metabolic syndrome (E1, E7, I1)
between breakfast skippers and normal eaters of recipients of health-check in FY2008
rule-out diagnoses excluded

Figure 21 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between breakfast skippers and nonskippers

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Evaluation of the health check up and guidance program through linkage with health insurance claims

Figure 22 CUMULATIVE INCIDENCE of metabolic syndrome (E1, E7, I1)
between drinkers and non drinkers of recipients of health-check in FY2008
rule-out diagnoses excluded

Figure 23 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between drinkers and nondrinkers

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Etsuji OKAMOTO, Yoshimune HIRATSUKA, Koichi OTSUBO, Toshiro KUMAKAWA

Figure 24 CUMULATIVE INCIDENCE OF NEWLY DIAGNOSED METABOLIC RELATED DISEASES (ICD10:E1, E7, I1)
between those who received health guidance and those who did not among those categorized into
aggressive or motivational intervention in health checks in FY

Figure 25 CUMULATIVE OUTPATIENT MEDICAL CHARGES
between those who received health guidance and those who did not among those categorized into
aggressive or motivational intervention in health checks in FY2008 (age<65, both sexes, N=415)

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Evaluation of the health check up and guidance program through linkage with health insurance claims

One of the results of policy goals relating to the HC&G
program targeting metabolic syndrome was obtained byanalyzing HIC data. HIC data provide valuable information
not only on medical charges but also on the incidence oftargeted diseases, both of which serve as outcomes toevaluate the HC&G program. To facilitate the use of HIC
data, computerization of HIC was promoted [8].
Computerization of HIC was almost achieved by fiscal 2011
and now health insurers have ready access to HIC data.
In fisal 2013, all health insurers are expected to evaluate
the outcomes of HC&G provided during the five years fromfiscal 2008 to 2012. By linking HC&G data with HIC data,
new evidence not known so far will be illuminated. The
weak or even opposite relationships between lifestylefactors and incidence and outpatient medical charges
demonstrated by this study and by Suzuki-Saito are new
evidence to be confirmed by follow-up studies. However,HIC data are complicated and its format is not designed
for data analysis [9]. It requires special skills to draw
meaningful information from them.
We demonstrated an example of evaluating the HC&G
program by linking with HIC data with a relatively longobservation period of 53 months. Not only did we compare
the total medical charges for all diseases, we compareddisease-specific charges by singling out metabolic-relateddiseases through objective methods. We found that (1)health risks detected in health check ups are reasonably
accurate in predicting the future incidence and medical
charges of targeted diseases, (2) lifestyles obtained fromquestionnaire are less accurate in predicting future
incidence and medical charges for metabolic relateddiseases and (3) those who received health guidance
showed lower incidence and medical charges than those
who did not. As for lifestyles factors, we came up withsomewhat negative conclusions: smoker and drinkers
showed lower incidence and medical charges for both all
diseases and metabolic-related diseases. Modification oflifestyles, such as eating habits, is not likely to lead tosavings in health care expenditure at least in a short periodof 53 months.
We have to acknowledge limitations from our findings in
drawing any cause-effect relationship. Our findings are
essentially observational ones with no control groups ormatching. No cause-effect relationship may be drawn
without some sophisticated methods such as propensity
score matching. At least the methodology presented in thisstudy will provide an example to all health insurers inevaluating the outcome of the HC&G program.

Acknowledgement
An excerpt of this article was presented at the 71st
annual meeting of Japanese Society of Public Health held at
Yamaguchi on October 25, 2012.

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and intervention program aimed at preventing
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[4] Okamoto E. Cost-benefit of health promotion. In: Muto
T, Nam EW, Nakahara T, editors. Asian Perspectives
and Evidence on Health Promotion and Education.
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[6] Okamoto E. Do individualized health promotional
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[8] Okamoto E. Online submission of health insurance
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レセプト突合による特定健康診査・保健指導の評価
抄録［目的］静岡県三島市国民健康保険の特定健康診査・保健指導データとレセプト情報とを突合し，健診から把握される健康リスク，質問票から把握される生活習慣ならびに特定保健指導受診の有無と，健診後のメタボ関連疾患発症率ならびに医科外来医療費を累積額で比較することによって同市の特定健康診査・保健指導事業の評価を試みた．
［方法］２００
８年度三島市が実施した特定健康診査・保健指導データを２
００８年６月〜２０
１２年１０月分の医科外来レセプトデータと個人単位で突合し，（１）健診から把握される健康リスク，（２）質問票から把握される生活習慣ならびに（３）メタボ症候群の有リスク者を対象とする特定保健指導受診の有無，の３要因を評価した．評価指標は，レセプトから把握されるメタボ関連疾患の発症率ならびに医科外来医療費（総医療費とメタボ関連医療費）の５３か月の累積額である．
［結果］健診から把握される健康リスクと特定保健指導受診の有無は，その後のメタボ発症率と医科
外来医療費に明確な差異がみられた．しかしながら,食習慣や運動といった生活習慣との関連は弱く，喫煙や飲酒では不健康な生活習慣の者の方が発症率や医科外来医療費が低いという予想とは逆の結果となった．
［考察］特定健康診査・保健指導データとレセプトデータを個人単位で突合することによって，特定健康診査・保健指導データ内のみの分析ではわからない結果が得られ,その中には予想外のものもあった．特定健康診査・保健指導の評価はレセプトデータとの突合も合わせて行う必要性が示唆された．
キーワード：メタボリック症候群，特定健康診査・保健指導，レセプト，生活習慣，比例配分法
岡本悦司，平塚義宗，大坪浩一，熊川寿郎
国立保健医療科学院医療・福祉サービス研究部

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J. Natl. Inst. Public Health, 62（1）: 2013