保健医療科学

2017Vol.66 No.6 p.640－649＜ Original ＞
Seasonal effects on healthcare costs: Using panel data from medical
and long-term care claim records of the elderly in Hokkaido
Naomi Akiyama1), Takeru Shiroiwa2)
1）

Iwate Medical university, School of Nursing, Division of Community-based Integrated Care2）

Department of Health and Welfare Service, National Institute of Public Health

Abstract
Objectives: Most developed countries share a growing concern about care services and increasing
healthcare expenditures. In this regard, many studies have identified the impact of seasons effects on
health, but have not clarified the relationship between seasons and healthcare services and costs. The aimof this study was to examine the impact of seasons on the healthcare expenditures; we used a longitudinalcohort of older persons for healthcare utilization and costs.
Methods: This paper describes the results of a longitudinal retrospective study conducted in City A,
Hokkaido, with a panel survey of medical care (MC) and/or LTC utilization and costs. The subjects werecommunity-based elderly residents aged 65 years and above (n = 1,793) who are classified as requiring
Support Levels 1 or 2, or Care Needs Levels 1 to 5, in accordance with the LTC insurance system. The
study used data from July and December of the years 2007 to 2009 (i.e., a total of six survey points). The
data were collected from MC insurance claim records, LTC insurance claim records, and questionnaires
used to evaluate the cognitive and physical status of each subject in order to ascertain his or her nursing
care needs in terms of the LTC insurance system.
Results: In 2007, the mean age of the subjects was 84 years, 71% of them were female, most had cognitive
impairment and/or functional disability, and most were on low incomes. There were no significant various
in the seasonal and yearly data for the MC and LTC costs. Probit analysis was conducted to determine
factors that are associated with the coefficients of healthcare behaviors per month. Winter season was anegative determinant of the utilization of outpatient care and positive determinant of the use of facility care.
Furthermore, we used a Tobit model to analyze the determinants of the coefficients of healthcare costs
per month. Winter season was found to be a negative determinant of outpatient costs, but had no impact
on other costs. Of the population variables, severe cognitive impairments and functional disabilities wereassociated with greater hospital and/or facility care costs.
Conclusion: Seasonal variations influence the use of healthcare services in the elderly who are in needof support or care. The population of elderly is growing and the MC and LTC costs are rising as the babyboomer generation is aging; thus, healthcare policymakers should pay attention to the relationship between
seasonal variations and healthcare utilization and costs.
keywords: Healthcare services, medical care cost, long-term care cost, seasonal effect, elderly.
(accepted for publication, 30th October 2017)

I．Introduction
As the baby-boomer generation ages over the next three
decades, demand for healthcare services are expected
to rise steeply [1]. This is particularly relevant in Japan,

which is experiencing the highest rate of aging of thesociety in the world. Japanʼs healthcare policy includes
medical care (MC) and long-term care (LTC) insurance
systems. With regard to the former, the country has had
a national health insurance (NHI) system that supports

Corresponding author: Naomi AKIYAMA
2-1-1 Nishitokuta, Yahabacho, Shiwa-gun, Iwate, 028-3694, Japan.
Tel: 019-651-5110(ex.5415) / Fax: 019-611-2088
E-mail: nakiyama@iwate-med.ac.jp

640

J. Natl. Inst. Public Health, 66 (6) : 2017

Seasonal effects on healthcare costs: Using panel data from medical and long-term care claim records of the elderly in Hokkaido
MC for all citizens since 1961. In 2008, this NHI system
was revised to include a new medical insurance system
for those aged 75 years and over. LTC insurance, a social
insurance system that supports LTC for the elderly, wasestablished in 2000 [2,3]. In 2010, the total costs for MC
insurance reached US$400 billion (US$1 = 93.5 yen in2009). Of this, US$222 billion represented the costs for theelderly. Further, total LTC costs have grown from US$42
billion in 2000 to US$85 billion in 2010. Such runaway
healthcare costs make the sustainability of the MC and
LTC insurance systems doubtful. In order to reduce theincreasing healthcare service costs, it is necessary toinvestigate the determinants of the inflated expenditures
for the elderly. The costs of facilities such as nursing homes
have been suggested as being one of the factors for theincreasing healthcare costs [4]. Thus, policy makers must
focus on ways to reduce the costs of healthcare facilities
and minimize admission of the elderly to such facilities.
Studies have identified seasonal impacts on health. For
example, seasonal variations have been reported on thenumber of hospital admissions in the fields of general
medicine and orthopedics [5]. During the winter, increased
mortality has been reported in intensive care units [6]. In
particular, the elderly are vulnerable to very hot or cold
weather; for example, the incidence of hip joint fractures ishigher in the winter than in the spring season [7]. Rolden etal. used the data of elderly Dutch health insurance clientsand reported that institutionalization rates peak in thewinter [8]. Healthcare costs for the elderly in Japan has
paid by the MC and LTC insurance systems. The status ofindividuals can then be considered by using the data that
contain time-series observations and blending the interindividual differences with intra-individual dynamics [9].
However, in Japan it is generally difficult to combine theclaim records for MC and LTC; indeed, there are many
reports using MC records or LTC records, there is few
studies have analyzed the data both MC records and
LTC records. Consequently, a systematic analysis of therelationship between the seasons and healthcare costs has
not been conducted in Japan.
The behavioral model of utilization, developed byAndersen, Aday and others, is one of the most frequently
used frameworks for analyzing factors that are associatedwith patient utilization of healthcare services [10-12].
The framework of the model has broad applicability andenables analyses across age, country and various segmentsof society [13]. The model also includes four interacting
variables: environment, population characteristics,
health behavior, and outcomes. By 2025, when the babyboomer generation reaches the later-stage elderly, Japanis supposed to build an integrated community care systemthat takes regional realities into account. The environment
may be one of the factors that affect the health behaviorof elderly. If the effects of environmental factors on thehealthcare behavior were investigated, it will be useful for
construction of an integrated community system based on
local circumstances.
In this present study, the factors that influence healthcare
expenditures, especially the variables from Andersenʼsmodel related to the environment and population
characteristics, were examined. The subjects of the study
were elderly enrollees in the Japanese LTC insurance
system, who are responsible for a part of the MC and LTC
expenditures in local City A, Hokkaido, Japan. To determine
the seasonal effects on healthcare expenditures, we used
a longitudinal cohort of older persons for healthcare
utilization and costs.

II．Methodology
1．Study setting
The present study was conducted in City A of Hokkaido
prefecture, Japan, after the city extended its cooperation
for the research. City A is approximately in the centerof Hokkaido and has an inland climate. The averagetemperature is 5.5 ℃ , the mean annual rainfall is 960
mm, and the mean snowfall is 884 cm (data from 1980 to2010) [14]. During the summer season of 2009 (aroundJuly), the highest temperature was 30.6 ℃ and the lowest
was 9.6 ℃ . In contrast, during the winter season of 2009
(around December), the highest temperature was 3.4 ℃
and the lowest was -19.7℃ [14].The city A has a populationof 31,000. In Japan, local cities, towns and villages with apopulations of under 50,000 account for more than twothirds of all municipalities. In 2009, elderly people aged over
65 years old accounted for 25.0% of the total population inCity A, a figure higher than the national average of 22.7%.
However, the per-capita MC and LTC costs for the elderly
were almost equal to the national rates.
City A has four hospitals and 10 clinics. In addition, it has
one LTC health facility, two welfare facilities, and one LTC
medical facility. The proportion of LTC beds per 100,000
people is higher than the national average. The MC and
LTC services provided by City A are considered to becomparatively satisfactory.
2．Population
We conducted a survey of City A residents who wereover 65 years old and who were classified as requiring
Support Levels of 1 or 2, or Care Needs Levels of 1 to 5,
in accordance with the LTC insurance system. The survey
subjects were also insured by the NHI system and/or MC

J. Natl. Inst. Public Health, 66 (6) : 2017641

Naomi AKIYAMA, Takeru SHIROIWA
insurance system for elderly people aged over 65 years old.
We analyzed the data (n = 1,793) for the months of July andDecember of each year from 2007 to 2009 (i.e., a total of six
survey points).
In the Japanese LTC insurance system, eligibility isclassified into seven levels depending upon an individualʼscurrent cognitive/physical status and severity of their
medical condition(s). Individuals classified as requiring
Support Level 1 or 2 do not require help for daily living
activities; however, they could be potentially assigned in
the near future to a Care Needs Level. Such individuals
sometimes require some help, not care, for physicalfunctions in order to retain their independence during their
daily lives. Individuals classified into Care Needs Level
1 require partial care, whereas those classified into Care
Needs Level 5 cannot undertake any activities of daily living
without extensive assistance. A Care Needs Certification
Review questionnaire must be completed to determine thelevel that a person should be assigned to. A representativefrom the cityʼs administrative office or agent of home-careproviders commissioned by the city interviews the applicant
and his or her family to determine the applicantʼs cognitiveand physical status and the severity of his/her medical
conditions. The questionnaire contains 74–82 items andtwo scales, namely, to determine the degree of independent
living in elderly people with cognitive impairments and thedegree of independent living in elderly people with physicaldisabilities. Then, a committee comprising medical andother professionals reviews the results and assigns each
person to the appropriate Support Level or Care Needs
Level in accordance with the LTC insurance system [2].
Support and Care Needs Levels are classified according
to the number of services provided and the fee for eachservice.
3．DataA longitudinal design was applied to the analysis of theMC and LTC insurance data. The MC insurance data wereobtained from the NHI and/or MC insurance systems forthe elderly aged over 65 years old for the months of Julyand December of 2007, 2008 and 2009 (i.e., a total of six
survey points). The LTC insurance data were obtained
from the LTC insurance system and collected for the same
periods as the MC survey points.
A panel for each subject was constructed with the dataobtained from his/her MC and LTC insurance records.
The MC records contain information on the age and sexof the patient, and monthly information on hospital and/or
outpatient care service use, and the costs for such use. The
LTC records contain information on the age and sex of thepatient, and monthly information on the Support or Care

642

Needs level assigned to, home and/or facility care services
used, and the costs for such services. The LTC records
also contained information about the level of the LTC
premiums and the filled-in Care Needs Certification Review
questionnaire.
Subjects are requested to pay their LTC premiums alongwith their local residence tax. The insurance premium
for LTC is proportionate to an individualʼs income; thus,
if individuals with higher incomes pay a higher LTC
premium. The premium ranges from levels 1 to 6 in the
LTC insurance system of City A. Subjects who receive
welfare or an old-age welfare pension, and those who are
not required to pay the local residence tax, are assigned
to Level 1 or 2. Subjects who are members of households
that are exempt from municipal taxation are assigned to
Level 3 or 4. Subjects who pay municipal taxes, but whose
total annual income is less than 2,500,000 yen are assigned
to Level 5. Subjects who pay municipal taxes and whose
total annual income is 2,500,000 yen or more are assigned
to Level 6. The scales in the Care Needs Certification
Review questionnaire are used to determine the degreeof independent living in elderly people with cognitive
impairments and the degree of independent living in elderly
people with physical disabilities (functional disabilities).
Cognitive impairment is ranked as follows: Independent,
Rank I (mild cognitive disorder), Rank II, Rank III, Rank
IV, and Rank M (severe cognitive disorder). Functional
disability is ranked as follows: Independent, Rank J (requires
partial care), Rank A, Rank B, and Rank C (bedridden andunable to undertake any activities of daily living without
extensive assistance). The Care Needs Certification
Review questionnaire contains the question, “What kind of
medical care did you receive during the past 14 days?”; weused the response to this question to judge the severity ofthe subjectʼs medical condition(s). The data for each subject
at each of the six survey points were combined.
4．Analytical approach
We included the following variables: environment,
namely, the season (summer or winter); population
characteristics, namely, the age, sex, and income level(Levels 1–6); cognitive impairment (Independent–RankM); functional disability (Independent–Rank C); medical
condition (receiving/not receiving medical treatment); MC
services; MC costs; LTC services; LTC costs.
We selected variables that were considered to bepotentially associated with healthcare costs on the basisof prior research. Most studies conducted outside Japan
have used units of data with individual panel data or crosssectional data. The results of these studies revealed that the
population status, sex [15], cognitive impairment [15,16],

J. Natl. Inst. Public Health, 66 (6) : 2017

Seasonal effects on healthcare costs: Using panel data from medical and long-term care claim records of the elderly in Hokkaido
functional disability [15,17], and medical condition [18], areassociated with the healthcare costs. It should also be notedthat Japanʼs social security system for the elderly differsfrom those in other countries. For example, in the UnitedStates, Medicare is part of MC, whereas the LTC costs inJapan are part of the social security system. However, few
prior studies have used individual panel data on the healthand seasons in Japan.
Both Izumida and Akiyama et al. used the information on
the LTC premium as a surrogate parameter of the income
level in their studies [19,20]. The authors revealed thata low socioeconomic status influences the admission ofelderly to care facilities [20]. Izumida used the variableof income status, with Level 2 as low-income status andLevel 3 or more as high-income status. His study indicatedthat the number of elderly people with low incomes andassigned to high Care Needs Levels that are admitted to
LTC facilities is increasing [19]. In addition, Akiyama et
al. used the LTC premium as a surrogate marker of theincome. In their study, they classified Levels 1 and 2 into
the low-income level, Levels 3 and 4 into the middleincome level, and Levels 5 and 6 into the high-income
level [20]. We found that the number of assigned subjects
for income status was unbalanced. Thus, we combined
the income status levels 1–6 and divided the income level
into three ranks, namely, high, middle and low, in line withprior reports [19,20]. We also used information on the LTC
premium as a surrogate marker of the income level. Using
individual cross-sectional data, Akiyama et al. reported theexistence of a strong association between the LTC costs
and the Care Needs Levels and LTC costs are strongly
associated [21]. Care Needs Levels are determined by
the amount of time for which the insured person needs
care from other people. The levels are not determinedby the individualsʼ assigned severe Care Needs Levels,
the severity of cognitive impairments, and/or the degreeof functional disabilities. Studies about the elderly who
have Care Needs certification have used the degree of
independent living of the elderly and disabled as a surrogate
parameters of cognitive and functional disabilities [22,23].
We also used the two scales and the response to theaforementioned question in the Care Needs Certification
Review questionnaire as surrogate parameters of cognitive/functional and medical status. The number of subjects
with cognitive impairment classified into Ranks IV and M
was small; thus, we divided cognitive impairment into fiveranks, Independent, Rank I, Rank II, Rank III, and Rank IV/
M, in line with prior reports [22,23]. Most of the subjects
did not have medical conditions that required insulin
injections, tube-feeding, etc.
We divided season, which was an independent variable,

into summer and winter. The data for summer were fromthe observation points of July 2007, July 2008 and July 2009,
while those for the winter were from December 2007,
December 2008 and December 2009.
Subjects were classified as MC service users if they
had MC insurance claims. In order to survey the trendof use of MC services, we divided MC insurance claims
into two: hospital use and outpatient use. With regard
to LTC services, we divided LTC insurance claims into
two types: home care and facility services. In 2008, theJapanese MC insurance system was revised and thetotal remuneration for MC costs decreased by 0.82%. In
2009, the LTC insurance system was also revised andthe total remuneration for LTC costs increased by 3%.
Considering the effects of these changes to the MC and/or
LTC insurance remunerations, we multiplied the MC costin 2007 by -0.82% and the LTC costs in 2007 and 2008 by+3% for 2009.
Data were analyzed using the t-test, the Wilcoxon
signed-rank test, and the median test to compare thecharacteristics and costs between summer and winteracross the years 2007, 2008 and 2009. The Probit model
was used when the dependent variable was a binary
variable, like healthcare utilization, and the Tobit model
was used when the dependent variable was represented
by truncated data, like healthcare costs. We assigned thesummer values as the reference values for the seasonalvariables. In addition, we assigned female as the reference
value of sex. In this study, high-income level (Levels 1 and
2) was set as the reference, and middle-income level (Levels
3 and 4) as one level lower, and low-income level (Levels
5 and 6) as two levels lower. Using independent as thereference in the classification of cognitive impairment, we
ranked the other levels in descending order of severity as
follows(Rank I = 1, Rank II = 2, Rank III = 3, Rank IV/M
= 4). Similarly, the number of subjects classified according
to the degree of functional disability as Independentand into Rank J was so small that we divided functional
disability into four ranks: Independent/Rank J, Rank A,
Rank B, and Rank C. Independent/Rank J was used as thereference value, and we assigned the patients in order fromslight functional disability to severe functional disability as
follows” Rank A = 1, Rank B = 2, and Rank C = 3. Persons
who did not need medical care were assigned the rank ofzero, and those who needed one or more kinds of medicalcare were assigned the group I or higher. We assigned thezero group as the reference group.
A random-effects Probit model was used to investigate
the effect of season on healthcare. Since data on all thevariables were not available all points in time, we selected
random-effect which is the individual-specific effect, instead

J. Natl. Inst. Public Health, 66 (6) : 2017643

Naomi AKIYAMA, Takeru SHIROIWAof the fixed effect. The analysis was performed using
healthcare services as the dependent variable and season,age, sex, income status, cognitive impairment, functional
disability, medical condition, and year (2007, 2008, and2009) as the independent variables. The distributions of
the MC and LTC costs were censored at zero for a large
portion of the sample. In order to examine the seasonal
effects, we used a Tobit (1958) model for estimating a
regression function with a censored dependent variable[24]. In the Tobit model, data are disconnected at a levelof 0 dollar of healthcare cost. The analysis was performed
with healthcare cost as the dependent variable, and season,age, sex, income status, cognitive impairment, functional
disability, medical condition, and year as the independent
variables. Costs and age were treated as continuous
variables, while healthcare, sex, medical condition, year,
use/no use of services, and costs were categorical variables.
Income status, cognitive impairment, and functional
disability were treated as ordinary variables. As in the
Probit model, the Tobit model also handles the reference
variables the same way.
The statistical analyses shown in Table 1 and Table 2
were performed using the JMP Statistical Software, version
9.0 (SAS Institute Inc., CA, USA), and the time-series
analyses shown in Table 3 and Table 4 of were performed
using Stata, version 11.0 (Light Stone Inc., TX, USA). P ≦0.05 was considered as denoting statistical significance in all
the analyses. This study was conducted with the approval of
the Ethics Committee of the University of Tokyo. It was a
joint study between city A and the researchers. We obtained
Table 1

Income status c

Cognitive impairment c

Functional disability cMedical condition b

aIII．Results
1．Subjectsʼ characteristics and monthly healthcare
costs
The extracted data set consisted of 1,793 subjects, ofwhich information for all six survey points were obtainedfor 733. In the remaining 1,060 subjects, informationfrom fewer survey points were obtained because ofdeath, departure from City A, or the non-assignment ofCare Needs or Support levels for certain survey points.
We excluded seven subjects because of missing data for
the independent variables. Overall, we used a data set
consisting of 1,786 subjects and 7,291 observations.
Table 1 shows the characteristics of the 1,786 subjects. In
2007, the mean age of the subjects was 84 years and 71% of
the subjects were female. In addition, most of the subjects
had low incomes, Rank II cognitive impairment, and Rank
A functional disability. Cognitive impairment and functional
disability were widespread: nearly 80% of the subjects
needed help. Most of the subjects were assigned a valueof zero because they had medical conditions. We foundno significant differences in the subjectsʼ characteristics
between the seasons by years. Although we have notshown the distribution of the LTC Levels in the table, thedistribution was as follows: Support Level 1/2, 30%; Care

A comparison of subjectsʼ characteristics between seasons by years
Summern = 1,166

Year 2007
Wintern = 1,209

mean ± SD 83.9 ± 7.7

P_value

Summern = 1,250

Year 2008
Wintern = 1,208

Summern = 1,242

Year 2009
Wintern = 1,216

P_value

83.9 ± 7.6

0.887

84.2 ± 7.7

84.1 ± 7.7

0.652

83.8 ± 7.5

83.8 ± 7.4

0.905

n (%)

825 (70.8)

852 (70.6)

0.893

873 (69.8)

846 (70.0)0.930

874 (70.4)

855 (70.3)

1.000

Male

n (%)

341 (29.2)

357 (29.5)377 (30.2)

362 (30.0)

368 (29.6)

361 (29.7)

High

n (%)

142 (12.2)

143 (11.8)

0.502

161 (12.9)

165 (13.7)

0.641

169 (13.6)

172 (14.1)

Middlen (%)

493 (42.3)

538 (44.5)

520 (41.6)

501 (41.5)

517 (41.6)

507 (41.7)

Low

n (%)

531 (45.5)

528 (43.7)

Independentn (%)

229 (19.6)

217 (17.9)

569 (45.5)

542 (44.9)

220 (17.6)

205 (17.0)556 (44.8)

537 (44.2)

219 (17.6)

213 (17.5)

Rank Ⅰn (%)

302 (25.9)

331 (27.4)

321 (25.7)

313 (25.9)301 (24.2)

281 (23.1)

Rank Ⅱn (%)

Rank Ⅲn (%)

398 (34.1)

394 (32.6)

154 (13.2)

185 (15.3)

426 (34.1)

427 (35.3)

450 (36.2)

451 (37.1)

206 (16.5)

184 (15.2)

193 (15.5)

Rank IV/Mn (%)

83 (7.1)

82 (6.8)

189 (15.5)

77 (6.2)

79 (6.5)

79 (6.4)

Independent/Rank Jn (%)

263 (22.6)

289 (23.9)

82 (6.7)309 (24.7)

313 (25.9)

341 (27.5)340 (28.0)

Rank An (%)

513 (44.0)

509 (42.1)

513 (41.0)

502 (41.6)

533 (42.9)

534 (43.9)

Rank Bn (%)

Rank Cn (%)

247 (21.2)

259 (21.4)

289 (23.0)

264 (21.9)

256 (20.6)

238 (19.6)

143 (12.3)

152 (12.6)

140 (11.2)

129 (10.7)

112 (9.0)

Zero

n (%)

1031 (88.4) 1073 (88.8)

104 (8.6)

One or moren (%)

135 (11.6)

136 (11.2)

0.468

0.845

0.847

1132 (90.6) 1092 (90.4)
118 (9.4)

116 (9.6)

t-test; bFisherʼs exact test; cWilcoxon signed-rank test.

644

P_value

Female

Age a
Sex b

comprehensive agreement from the residents for the study.
In order to preserve the anonymity of the participants, thestaff of city A deleted all their personal information before
handing it over to the researchers.

J. Natl. Inst. Public Health, 66 (6) : 2017

0.944

0.353

0.891

1132 (91.1) 1117 (91.9)
110 (8.9)

99 (8.1)

0.701

0.629

0.509

0.563

Seasonal effects on healthcare costs: Using panel data from medical and long-term care claim records of the elderly in Hokkaido
Table 2

A comparison of the utilization of healthcare services and costs between seasons by years
Summern = 1,166

Year 2007
Wintern = 1,209

P_value

Summern = 1,250

Year 2008
Wintern = 1,208

P_value

Summern = 1,242

Year 2009
Wintern = 1,216

P_value

The utilization of healthcare services
Medical care services usea

Hospitaln (%)

189 (16.2)

186 (15.4)

0.779

210 (16.8)

163 (13.5)

0.021

140 (11.3)

154 (12.7)

0.291

Outpatientn (%)

826 (70.8)

810 (67.0)

0.039

905 (72.4)

828 (68.5)

0.034

863 (69.5)

892 (73.4)

0.036

Long-term care services usea Home caren (%)

565 (48.5)

578 (47.8)

0.626

607 (48.6)

605 (50.1)

0.444

637 (51.3)

624 (51.3)

1.000

Facility caren (%)

257 (22.0)

262 (21.7)

0.883

272 (21.8)

269 (22.3)

0.770

263 (21.2)

279 (22.9)

0.307

Heatlhcare costs
Monthly medical care costsb

HospitalMedian

0.0

0.0

0.582

0.0

0.0

0.022

0.0

0.0

0.288

OutpatientMedian

60.5

41.9

0.002

54.7

50.8

0.747

56.5

59.5

0.420

Median

0.0

0.0

0.853

0.0

31.8

0.497

43.0

36.8

0.936

Facility care cost Median

0.0

0.0

0.794

0.0

0.0

0.716

0.0

0.0

0.812

Monthly long-term care costsb Home care costaFisherʼs exact test; bMedian test.

Needs Level 1, 20%; Level 2, 17%; Level 3, 14%; Level
4, 9%; and Level 5, 10%. The observed ratios of Support
Levels and Care Needs Levels were the same as thenational levels in the winter of 2007 [25].
Table 2 presents a comparison of the subjectsʼ healthcare
services and costs. Approximately 70% of the subjects
were outpatients who used MC services. Furthermore,
50% of the subjects used home care services within the
LTC insurance system. The medians of the hospital costsin the MC insurance system and facility costs in the LTC
insurance system were both US$0 across the survey
points. However, the medians of the outpatient costs andsome of the home care costs were $42–$61 and $32–$43,
respectively. There were no significant various in theseasonal and yearly data for the MC and LTC costs.
2．Probit analysis: Impact of season on the utilizationof healthcare services
Table 3 summarizes the results of the Probit analysis.
This analysis was conducted to determine the factors
Table 3

associated with the coefficients of healthcare behaviors permonth. Season was found to be a negative determinant of
the utilization of outpatient care and positive determinantof facility care. In the Probit model, the likelihood of
outpatient care use decreasing during winter as comparedto the summer. Conversely, the likelihood of facility careuse increased during winter as compared to the summer.
The results of the relationship between the subjectsʼcharacteristics and healthcare service use showed that
increasing dependence among those with cognitive
impairments and functional disabilities are risk factors
for increased use of hospital and facility care in the study
cohort. Moreover, decreasing dependence among subjects
with cognitive impairments and functional disabilities
was a significant factor contributing to decreased use of
outpatient and home care. Severity of the medical condition
was a significantly positive determinant of hospital use. In
contrast, it was negatively associated with outpatient, homecare, and facility care. Low-income status was strongly andpositively associated with the utilization of facility care andProbit model; Analysis of seasonal effect impacts on the utilization of healthcare service among the elderlyn = 1,786
Medical care services
Hospital
Outpatient
P_value
P_value
Coef.
Coef.

Long-term care services
Home care
Facility care
P_value
P_value
Coef.
Coef.

Seasonal effect (ref. = summer)-0.3130.534-0.0840.042-0.056

0.273

0.215

0.003

Age

0.003

0.590

0.031

<0.001-0.005

0.630

0.080

<0.001Sex (ref. = female)

0.043

0.685

0.023

0.812

0.149

0.376

-0.175

0.414

Income status (ref. = high income)-0.203

0.002

0.039

0.495

-0.168

0.049

0.800

<0.001

Cognitive impairment (ref. = Independent)

0.103

0.007

-0.157<0.001-0.331<0.001

0.616<0.001

Functional disability (ref. = Independent/Rank J)

0.623<0.001-0.477<0.001-0.925<0.0011.128<0.001

Medical condition (ref. = zero)

0.551<0.001-0.3060.006-0.610<0.001-0.421

0.033

Notes: Coef. is coefficient. Ref. is reference value.
P_values were calculated using a random-effect Probit model.
The Probit model was adjusted for the dummy variables of the appropriate year of the study.
Costs are US$/month; US$1 = 93.5 yen in 2009.

J. Natl. Inst. Public Health, 66 (6) : 2017645

Naomi AKIYAMA, Takeru SHIROIWA
was a contributory factor to increased LTC insurance. The
pattern of the Probit analysis results reveals that those withsevere dependency because of cognitive impairments and/or functional disabilities had a large coefficient for the use of
hospitals and/or facility care services. Moreover, those who
used hospitals required some medical care, and those who
used facility care services had low incomes.
3．Tobit analysis: Seasonal impacts on healthcare
costs
We used a Tobit model to analyze the determinants of thecoefficients of healthcare costs. Table 4 shows the factorsthat are related to the healthcare costs per month. The
winter season was a negative determinant of outpatient
costs, but was not associated with other costs. In the Tobit
model, outpatient costs were lower by 19 dollars per month
during the winter as compared to the summer.
Among the variables in the research population, severe
cognitive impairments and functional disabilities generated
greater hospital and/or facility care costs. The negative
parameter estimated for the variables of the income statusindicated that the hospital costs were lower for subjects
with low incomes than for those with high incomes, forwhom the facility care costs were higher.

IV．Discussion
The primary aim of this retrospective longitudinal cohort
study was to examine the seasonal effects on the healthcare
expenditures of older persons. When considering factorsthat influence healthcare costs paid by the MC and LTC
insurance systems, it is necessary to collect and combine
various data from both sets of claim records for each
person. However, only a few studies have simultaneously
Table 4analyzed the data from both MC and LTC claim records.
The reason is that the insurers of MC and LTC differ intheir approaches; thus, it is generally difficult to combine
MC and LTC claim records. In this study, we were fortunate
to obtain the permission of City A to collect various data
in a single panel for each subject from both types of claim
records. We were able to collect various data in a single
panel for each subject from both claim records.
Three major findings of this study should be emphasized.
First, seasons exert an influence on the level of use of
outpatient and facility care services and consequently
outpatient costs. With regard to outpatients, the utilization
rate and costs in the winter were lower than those in thesummer. In the area examined in this study, the impact ofclimate, such as heavy snowfall and geography (e.g., long
distances between houses), makes the provision of careto the elderly living at home a challenge. Furthermore,
the total amount of physical activity is influenced bymeteorological factors, particularly snow precipitation andthe mean ambient temperature [26]. It is possible thatnew health problems arise because of not being able toreceive the necessary MC. Therefore, when considering an
integrated community system of the elderly, it is necessary
to pay attention to the influence of weather conditions
on MC and LTC. In this study, we did not consider the
influence of the difference in the number of businessdays of the hospital between July and December. The
smaller number of business days in December could havecontributed to the decrease in the use of outpatient services
and outpatient costs in the winter.
Second, we found that the use of facilities increased in
the winter as compared to the summer. A prior study also
reported that institutionalization rates are significantly
higher in the winter [5]. During the winter, the elderly

Tobit model; Analysis of seasonal effect impacts on healthcare costs among the elderly
Medical care costs
Hospital
P_valueCoef.

n = 1,786

Long-term care costs

Outpatient
P_value
Coef.

Home care
P_value
Coef.

Facility care
P_value
Coef.

Seasonal effect (ref. = summer)-167.8

0.480

-18.6

0.013

-17.7

0.088

27.3

0.457

Age

-4.4

0.875

1.7

0.268

-9.9

0.006

80.8

<0.001Sex (ref. = female)

57.9

0.906

31.1

0.218

106.7

0.086

-379.4

0.021

Income status (ref. = high income)-1005.7

0.001

-4.6

0.726

-30.8

0.166

212.0

0.004

Cognitive impairment (ref. = Independent)

422.7

0.018

-34.7

<0.001

-26.6

0.029

217.4

<0.001

Functional disability (ref. = Independent/Rank J)

2533.2<0.001-95.9<0.001-181.7<0.001870.1<0.001

Medical condition (ref. = zero)2090.6<0.001127.4<0.001

-71.0

0.073

-202.6

0.073

Notes: Coef. is coefficient. Ref. is reference value.
P_values were calculated using a random-effect Tobit model.
The Tobit model was adjusted for the dummy variables of the appropriate year of the study.
Costs are US$/month; US$1 = 93.5 yen in 2009.

646

J. Natl. Inst. Public Health, 66 (6) : 2017

Seasonal effects on healthcare costs: Using panel data from medical and long-term care claim records of the elderly in Hokkaido
cannot stay at home alone if they have severe disabilities.
They should be admitted to facilities temporarily, such as
in short LTC health facilities. Problems such as snow andlow temperatures during the winter make it difficult for
the elderly living at home to go out. It has been reported
that severe cognitive impairment and functional disability
are associated with a decrease in the outpatient and homecare service use and costs, and conversely, with increased
hospital and facility care service use and costs. This is inline with the findings of this research which revealed thatthe level of cognitive impairment and functional disability
were determinants of healthcare expenditures [15,17]. In
addition, according to the staff of an LTC health facility,
some elderly people living alone find it difficult to stayat home during the winter and choose to be admitted toan LTC facility. Consequently, our findings revealed theimpact of seasons on the health behaviors of functionallydisabled elderly. There is a possibility of seasonal effects
being particularly strong in persons with severe cognitive
impairment and functional disability, although this remains
to be examined in a future study.
Third, the variable of income level and medical condition
was different directions associated with hospital and facility
care use and costs. This points show that the people who
expenditure hospital care has high income and medicalcare. Conversely, the people who expenditure facility careexpenditure had low income and without being taking
medical care. A previous study reported that people with
low incomes tend to select facilities as their living quarters[19]. There is a possibility that elderly people choose theirliving quarters based on their income and/or medical careneeds. It could also be dependent on the level of cognitive
impairment and functional disability (independent orseverely dependent). Thus, the income level, medical careneeds and degrees of cognitive impairment, functional
disability may be associated with the choice of an individual
in respect of the living quarters, namely, care facility orhome (persons who use both home care and outpatientcare). From the research results of this study for the elderly
conducted using individuals MC and LTC data, it became
clear that the LTC need is related to the consumption ofMC.
Our study had some limitations. First, we conducted
our analyses in just one city; thus, caution should be
exercised while generalizing the results to areas with larger
populations. We did not use a randomized design, therefore,
we cannot conclude a causal relationship. Second, priorstudies have indicated the relationship between healthcare
costs and variables such as illness, living arrangements, andmarital status. However, these factors were not included in
the present study because information pertaining to thesefactors was not available in the claim records.
Despite these limitations, our findings indicate thatenvironmental factors such as seasonal effects should beincorporated in the behavioral models of healthcare services
for the elderly. Moreover, the results of the present study
yield several findings that could inform recent discussions
about ways in which more integrated community care forfrail older adults should be designed in the years up to 2025.
Healthcare policies for the elderly should focus on seasonal
effects on outpatient and facility services expenditures.

V．Conclusion
Seasonal factors influence the use of healthcare services
for those elderly who are in need of support or care. The
elderly are growing in number and MC and LTC costs arerising as the baby-boomer generation ages; thus, healthcare
policymakers should pay attention to the relationshipbetween seasonal effects and healthcare utilization andcosts.

ACKNOWLEDGEMENTS
This study was conducted with funding from the Institutefor Health Economics and Policy (2009). We extend ourappreciation to the staff of City Aʼs Health Insurance Officefor their assistance with the data preparation.

COI
This study was conducted with funding from the Institutefor Health Economics and Policy (2009). We did not receive
any commercial fund, but only research grants as a partof social contribution to the group. We did not receive any
funds related to employment, consultancy, patents, products
under development, marketed products, etc. We did notshare any data or materials with the company.

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Seasonal effects on healthcare costs: Using panel data from medical and long-term care claim records of the elderly in Hokkaido

医療保険と介護保険のパネルデータを用いたヘルスケアコストへの季節要因の影響
秋山直美 1），白岩健 2）1）

岩手医科大学看護学部地域包括ケア講座

2）

国立保健医療科学院医療・福祉サービス研究部

抄録
目的：ベビーブーマー世代が後期高齢者を迎える2025年に向けて，我が国は地域で在住する高齢者に対する包括ケアの創設に苦慮している．本研究は要介護認定高齢者におけるヘルスケアサービスの受療行動に対する季節要因の影響を検討し，高齢者対策に向けた示唆を得ることである．
方法：本研究では研究協力の得られた北海道A市の医療保険と介護保険の後向きパネルデータを使用
した．要支援 1，要支援 2 ，要介護 1 ～ 5 の認定を受けている65歳以上の要介護認定高齢者1793名を対象とした．データは2007～2009年の 7 月，12月の 6 調査時点の医療レセプトと介護レセプト，要介
護認定調査の調査結果を収集し，個人毎に突合した．ヘルスケアサービスの利用率と季節要因の関連を調べるため，1ヶ月のヘルスケアサービス（入院医療／外来医療／居宅／介護施設サービス）の利用の有無を従属変数，個人属性（年齢・性別・所得・認知度・寝たきり度）を独立変数としたプロビッ
ト分析を行った．同様に，1 ヶ月のヘルスケアサービス費（入院医療費／外来医療費／居宅サービス費／施設サービス費）を従属変数，個人属性を独立変数とするトービット分析を行った．
結果：対象者の2007年の年齢は84歳，71％は女性であった．対象者のほとんどは認知機能と身体機能に障害があり，収入は低かった．対象者の属性には季節と年度によって差はなかった．外来サービスの利用率は夏季に比べて冬季で減少し，介護保険施設サービスの利用率は夏季に比べて冬季に上昇することがプロビット分析で明らかになった．また，外来医療費は夏季に比べて冬季に減少することが明らかになった．属性とヘルスケアサービスの関係においては，重度な認知機能障害があるほど，身体機能障害があるほど，入院医療費や介護保険施設サービス費が有意に増加することがトービットモ
デルで明らかになった．
結論：本研究の結果から，要介護認定高齢者では季節によりヘルスケアサービスの利用に影響する可能性が示唆された．高齢者の地域包括ケアを考慮する際は，気象条件による医療・介護サービスへの影響を留意する必要がある．
キーワード：ヘルスケアサービス，医療費，介護費，季節要因，高齢者

J. Natl. Inst. Public Health, 66 (6) : 2017649