73

Global Warming, Cedar Pollinosis, andHealth Care Budget Impact
Masahide KONDO1*, Aiko SHONO2 and Yasushi HONDA1
1

Department of Health Care Policy and Management, University of Tsukuba,
1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8577 Japane-mail: mkondo@md.tsukuba.ac.jp
2
Doctoral Program in Human-Care Sciences, University of Tsukuba,
1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8577 Japan*corresponding author

Abstract
Cedar pollinosis is one of the most common illnesses in Japan. It is known that the medical careseeking by patients with severe symptoms depends on the scatter of pollen, of which production isaccelerated by the warmth of the previous summer. It is also known that high prevalence, 10% to 20%of the nation, results in a considerable amount of health care expenditure. This study aims to illustrate
an impact of climate change by quantifying the health care budget impact of cedar pollinosis underglobal warming with scenarios of 1°C to 5°C warming ceteris paribus through combining the available
knowledge in the literature. The estimated budget impact, 4,343 million yen with 1°C warming,
9,019 million yen with 2°C, 13,031 million yen with 3°C, 17,372 million yen with 4°C and 21,717
million yen with 5°C, is approximately 0.016% to 0.070% of the national health care budget of the late
1990s. It is suggested that the climate change in a form of global warming causes a financial burden on
the government or the society, threatening the sustainability of social insurance based health system, or
public finance. Our estimation depends on a number of assumptions such that “with other things being
the same”. Despite of these limitations, we conclude that the government needs to pay attention to thehealth care budget impact of cedar pollinosis and consider actions and priorities that adapt to global
warming.
Key words: budget impact, cedar pollinosis, climate change, global warming, health care,seasonal allergic rhinitis

1. Introduction
Seasonal allergic rhinitis, or pollinosis, is one of
the most common climate related illnesses in Japan.
Ten to twenty percent of the nation suffer from thedisease (Okuda, 2003; PG-MARJ committee, 2005).
The pollen of Japanese cedar (Cryptomeria japonica)
is a dominant allergen, which scatters in the air fromwinter to spring every year, and weather forecasts
report atmospheric pollen readings everyday during
the seasons, so that the sufferers can manage their
exposure to allergen.
The climate model of cedar pollinosis is generally
understood to indicate that the incidence of new cases
and the severity of symptoms in diagnosed cases areaffected by the level of atmospheric pollen scatter(Baba & Sasaki, 1997; Baba, 1998), which is affectedby climate factors (Murayama, 1996, 1998). Among
Global Environmental Research
11: 73-78 (2007)
printed in Japanthe climate factors, the warmth of the previous
summer is identified as one of the most significant
determinants of the level of pollen scatter. It isbotanically explained that the growth of cedar male
flowers, which scatter pollen during the seasons, isencouraged by the warmth of the previous summer(Yokoyama, 2002; Sasajima et al., 2003). Given thisrelationship, the impact of global warming on themorbidity of cedar pollinosis becomes a matter ofdiscussion (Takahashi et al., 1996; Teranishi et al.,2000). Due to high morbidity, health care resources
deployed for the disease also become a matter ofdiscussion coupled with a growing concern about thefinancial sustainability of the social health insurance
system (Okuda, 1998; Kawaguchi et al., 2001). There
are, however, few studies, which aim to explore thesematters comprehensively. Past studies focus rather on
specific aspects of the climate model or health care©2007 AIRIES

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M. KONDO et al.

resource implications.
In this study, we aim to illustrate a broad picture byquantitatively estimating the health care resource
implication of cedar pollinosis under global warming
in a transparent way with available knowledge. Our
results expand the empirical knowledge on the consequences of climate change, which should informsocial policy to adapt to global warming.

2. Method
We assume a simple health care budget impact
model by extending the climate model of cedar pollinosis, which is shown in Fig. 1. It assumes that theclimate factor measured by air temperature is associated with allergen exposure measured by pollen count,which is also associated with medical care seeking
measured by outpatient visit and the accompanying
financial burden on the society, which consists ofdiverse economic entities, measured by medical expenditure. An underlying assumption about the relationship between pollen count and outpatient visit isthat an increase in the severity of symptoms caused byincreased allergen exposure results in an increase in
outpatient visit. Increased allergen exposure is also
assumed to increase sensitisation, which also contributes to an increase in outpatient visit.
The health care budget impact is defined as aforecast of rates of use (or changes in rates of use)with their consequent short- and medium-term effects
on budgets and other resources to help health service
managers (Culyer, 2005). The budget in the context of
this study is a part of national medical expenditure
financed by third party payers, such as insurers or thegovernment. In other words, it is a fund sociallypooled from social security contribution and tax for
health care under the social insurance system, which iscollectively spent for the provision of effective
services for the nation. The budget impact at this levelis regarded as a financial burden on the government,
which is responsible for maintaining stable health careprovision and financing for the nation.
We conduct a deliberate literature survey in order
to identify reports of the quantitative relationships
between components in the budget impact model. We
also survey estimates of medical expenditure for cedar
pollinosis. We search online databases such as
Japanica Centra Revuo Medicina, PubMed, CiNii andWebcat Plus with appropriate combinations of key
words such as “climate”, “global warming”, “pollen”,“pollinosis”, “rhinitis”, “medical care seeking”,“outpatient” and “medical expenditure”. At the sametime, we examine reference lists of relevant literature.
With identified knowledge, we calculate the budget

impact of pollinosis under global warming by the
degree of warming from 1°C to 5°C ceteris paribus.

3. Results
Table 1 shows the results of the literature survey ofthe quantitative relationship between air temperatureand pollen count. Our search offers a number of reports which find the correlation between pollen countand various climate factors such as the monthly meanof daily maximum air temperature, monthly mean air
temperature, monthly sunshine hours, monthly mean
humidity, monthly precipitation, etc. (Saito & Usami,
1980; Nemoto, 1988; Kawashima & Takahashi, 2002).
The significance of air temperature of the previous
summer as a determinant of pollen count is concluded
repeatedly in the majority of these papers (Takahashi& Kawashima, 1999; Kishikawa & Nishima, 2002).
However, quantitative relationships in a form of regression equation with air temperature as an independent variable are specified only in limited literature. We
identify seven air temperature – pollen count models:
the Tochigi model (Wang et al., 1984), the Toyama
model (Kenda et al., 1996), the Yamagata model(Takahashi et al., 1996), the Tokyo model (Bureau of
Public Health, 1998), the Okayama model (Nambaet al., 2000), the Chiba model (Sahashi, 2004) and the
Shizuoka model (Okuda et al., 2006). There are slightdifferences in the variables in these models. The
monthly mean air temperature of the previous summer
is used as an independent variable in the Tochigi
model, the Toyama model, the Yamagata model, theTokyo model and the Shizuoka model, while themonthly mean of daily maximum air temperature isused in the Okayama model and the Chiba model.
However, it is well-known that there is a strong
correlation between the daily mean air temperatureand the daily maximum air temperature. Cedar pollens
are counted in the Tochigi model, the Toyama model,
the Yamagata model and the Chiba model, while
Cupressaceae pollen count is included in the Tokyo
model, the Okayama model and the Shizuoka model.
We extract the contribution of air temperature topollen count increase, δt , in each model, leaving thesevariations untreated. δt is constant in linear regression
models: 2,326.42 counts/cm2·year·°C by the Durham
method in the Tochigi model; 1,280 and 1,267.6 in the
Yamagata model; 824.2 in the Tokyo model; 1,320,
1,155 and 1,476 in the Okayama model; 382.465 in
the Chiba model and 1,618.7 in the Shizuoka model.
In the Toyama model, δt is calculated under scenarios
in which the monthly mean air temperature increases
from 24.3°C, which is the mean of observed monthly
mean air temperatures holding other variables fixed atBudget impact

Climate factor
Allergen exposure
Health care seeking
Financial burden→ Outpatient visit → Medical expenditure
Air temperature→ Pollen countFig. 1

Budget impact model.

Global Warming, Pollinosis, and Health Budget Impact

their means. The results are: δt (1°C) = 1,716, δt (2°C)= 4,845, δt (3°C) = 10,553, δt (4°C) = 20,963, andδt (5°C) = 39,948 counts/cm2·year. The pollen count inthis model is highly responsive to air temperature,especially to high air temperature, because of the useof a logarithm in the equation. δt (3°C), δt (4°C) andδt (5°C) are too large compared to the maximum ofobserved pollen counts in the study, which is 11,854 at
2.3°C warmer than the normal value of 1995. With
this regard, we make adjustment in the estimation ofbudget impact.
Table 2 shows the results of the literature survey ofthe quantitative relationship between pollen count andoutpatient visit. There are a number of epidemiological reports that conclude incidence of new case corre-

75

lates to pollen count (Saito, 1997; Shida et al., 2000),
and pathological that conclude the severity of symptoms in diagnosed cases has a similar association(Takenaka, 1999; Baba, 2002). We, however, identify
only one pollen count – outpatient visit model: theIbaraki model (Tamura et al., 1995), which specifies aquantitative relationship in a form of regression equation. In this model, Cupressaceae pollen count is included in the independent variable, while the numberof outpatients with allergic rhinitis in April, which isconsidered to be dominated by cedar pollenosis, isused as a dependent variable. We extract the contribution of pollen count increase to outpatient visit increase in proportion, δop. Since the dependent variablein the equation is a ratio of medical care seeking per

Table 1 Air temperature – pollen count models.
1. Tochigi model (Observation period: 1979 - 1984)
y = 2,326.42x – 60,709.8
y: estimated total cedar pollen count, x: mean air temperature in summer of the previous year

→ δt* = 2,326.42
2. Toyama model (Observation period: 1983 - 1995)
Y = 0.26098X(1) – 0.01333X(2) + 0.00443X(3) + 0.00457X(4) – 2.73814
Y: logarithm of total cedar pollen count in a year, X(1): mean air temperature in July of the previous year, X(2): hours ofsunshine in January of the same year
X(3): snow cover in March of the same year, X(4): hours of sunshine in March of the same year
Estimated pollen count with means of observed X(1) = 24.3, X(2) = 71.3, X(3) = 22.6 and X(4) = 123.7 is 2083
With a scenario that mean air temperature increases from 24.3℃ ceteris paribus,

Wang
et al.
(1984)

Kenda
et al.
(1996)→ δt (1℃) = 1,716, δt (2℃) = 4,845, δt (3℃) = 10,553, δt (4℃) = 20,963, δt (5℃) = 39,948
Note: Maximum of observed X(1) = 11,854; therefore, δt (3℃) = 10,553, δt (4℃) = 20,963 and δt (5℃) = 39,948
may be out of the range of good fitness.
3. Yamagata model (Observation period: 1984 - 1996)
Pe = 1,280T7 – 0.528Pob – 24,729, Pe = 1,267.6T7 – 64.4MFb – 24,438
Pe: estimated total cedar pollen count, T7: mean air temperature in July of the previous year, Pob: observed total pollencount of the previous year,
MFb: amount of male flower in the previous year (%)→ δt = 1,280, 1,267.6
4. Tokyo model (Observation period: 1979-1995)
y = 824.2tm – 162.0hu – 6302.2
y: estimated total cedar pollen count, tm: mean air temperature in July of the previous year, hu: mean humidity in July of
the previous year
→ δt = 824.2
5. Okayama model (Observation period: 1984 - 2000)
Kojima site: Y = 1,320T – 2.0P – 36,384, Okayama site: Y = 1,155T – 0.4P – 32,484, Wake site: Y = 1,476T – 4.4P – 40,169
Y: expected total number of pollen grains per cm2/year, T: mean value of daily maximum air temperature in July of theprevious year, P: precipitation in July of the previous year
→ δt = 1,320, 1,155, 1,476
6. Chiba model (Observation period: 1982 - 2003)
Y = 382.465X – 9,257.825
Y: estimated total cedar pollen count, X: mean value of daily maximum air temperature from 11 July to 10 August of theprevious year
→ δt = 382.465
7. Shizuoka model (Observation period: not reported)[pollen count] = –176,614.5 + 699.9 × [year] + 1,618.7 × [mean air temperature in July of the previous year] +1.6 × [mean precipitation in July of the previous year]→ δt = 1,618.7

Takahashi
et al.
(1996)
Bureau of
PublicHealth(1998)
Namba
et al.
(2000)
Sahashi(2004)
Okuda
et al.
(2006)* Contribution of air temperature to pollen count increase.

Table 2 Pollen count – outpatient visit model.
1. Ibaraki model (Observation period: 1980 - 1992)
Y = 26.28X1 + 0.01214X2 – 2009
Y: medical care seeking per 100,000 population, X1: calendar year, X2: total cedar pollen count
Increase of outpatient visit in proportion{26.28X1 + 0.01214(X 2 + δ t ) − 2009} − (26.28X1 + 0.01214X 2 − 2009)
0.01214=δt
26.28X1 + 0.01214X 2 − 200926.28X1 + 0.01214X 2 − 2009*=

Tamuraet al.
(1995)

Observed mean medical care seeking per 100,000 population = 288→ δop† = 0.01214 / 288 = 0.00004215* Contribution of air temperature to pollen count increase. † Contribution of pollen count increase to outpatient visit increase in proportion.

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M. KONDO et al.

100,000 population, the coefficient of the pollen countis divided by the mean of those observed in the study
in order to make the product of δt and δop a proportional change, which produces δop of 0.00004215.
Table 3 shows the results of the literature survey of
estimates of medical expenditure for cedar pollinosis,
or allergic rhinitis. Medical expenditure for cedar pollinosis in 1998 is estimated by Kawaguchi et al., withwhich we calculate the budget, π, for pollinosis assuming a co-payment ratio of 0.3, therefore 82,040
million yen. Okuda (1998) and Practical Guideline for
the Management of Allergic Rhinitis in Japan (PGMARJ, 2005) estimate medical expenditure for allergic rhinitis including perennial allergic rhinitis. Okuda
also reports the number of outpatients by the month,with which we calculate the proportion of outpatientsfrom January to May, the months when cedar pollen
scatters according to a calendar of pollens in PGMARJ, as 0.5045. Combining these gives estimates ofbudgets: 28,820 million yen in 1995 from the Okuda
estimate, and 30,660 - 38,325 million yen in 1994
from the PG-MARJ estimate, respectively.
Our literature survey reveals that quantitative relationships available for our budget impact estimation
Table 3are never abundant although there are many studies
that underpin the relationships assumed in the budget
impact model. The equations and estimates found are
based on observations in various areas and various
years since 1980s. The definitions of the variables
used are not consistent across models as already mentioned. In order to deal with these shortcomings, we
make further assumptions in our estimation of budget
impact with the available knowledge.
The contribution of air temperature to pollen countincrease, δt , at national level is assumed to fall in arange from minimum to maximum among the available figures. The differences among the definitions ofvariables are regarded as negligible. Therefore, δtranges from 382.465 at 1°C warming to 8,093.5 at
5°C warming. The contribution of a pollen count increase to an outpatient visit increase in proportion, δop ,at national level is assumed to be constant, 0.004215.
The budget, π, is assumed to fall in a range from minimum to maximum among the available figures. Inflation is not taken into account. Therefore, π ranges
from 39,835 million yen to 82,040 million yen.
Table 4 summarises these and shows the resulting
budget impact. The contribution of air temperature to

Budget estimation.

1. Kawaguchi estimate (Observation period: 1998)
Medical expenditure for pollinosis: ¥ 117,200 million

Kawaguchi
et al. (1998)→ π*: 117,200 × 0.7† = 82,040

2. Okuda estimate (Observation period: 1995)
Medical expenditure for allergic rhinitis: ¥ 112,800 million
Proportion of outpatients from January to May in one whole year: 50.45%→ π: 112,800 × 0.5045 × 0.7 = 39,835
3. Practical Guideline for the Management of Allergic Rhinitis in Japan (PG-MARJ) estimate (Observation period: 1994)
Medical expenditure for allergic rhinitis: ¥ 120,000 – 150,000 million
Proportion of outpatients from January to May in one whole year: 50.45%‡→π: 120,000 – 150,000 × 0.5045 × 0.7 = 42,378 – 52,973

Okuda(1998)

PG-MARJ(2005)* Budget for cedar pollinosis. † 1- co-payment ratio. ‡ Adopted from the Okuda estimate.

Table 4 Air temperature – budget impact estimates.
Range of contributionof air temperature topollen count increase:δt1
382.465 – 2,326.42
2764.93 – 4,845
31147.395 – 10,553
41529.86 – 20,963
51912.325 – 39,948* Mid-point of the range.
Warming(℃)

Contribution ofpollen count to
outpatient visit:δop0.00004215

Range of contributionof air temperature to
outpatient visit:δπ = δt ×δop ×100 (%)
5.709*(1.612 – 9.806 )
11.82(3.224 – 20.42 )
24.66(4.836 – 44.48 )47.40(6.448 – 88.36 )88.22(8.060 – 168.4 )Budgetπ(¥ million)

39,835 – 82,040

Budgetimpact:δπ÷100×π(¥ million)
4,343 ( 642 – 8,045)
9,019 (1,284 – 16,754)
19,209 (1,927 – 36,492)
37,529 (2,569 – 72,490)
70,675 (3,211 – 138,140)

Table 5 Air temperature – budget impact estimates with adjustment*.
Adjusted range
Adjusted range
Contribution of
Adjustedof contribution
Budgetof contribution
pollen count tobudget impact:of air temperature toπof air temperature to
outpatient visit:δπ÷100×π
outpatient visit:(¥ million)
pollen count increase:δop(¥ million)δπ = δt ×δop ×100 (%)δt
1
382.465 – 2,326.42
5.709(1.612 – 9.806 )
4,343 ( 642 – 8,045)2764.93 – 4,845
11.82(3.224 – 20.42 )9,019 (1,284 – 16,754)
0.00004215
13,031 (1,927 – 24,136)
3
1147.395 – 6,979.26
17.13(4.836 – 29.42 ) 39,835 - 82,040
17,372 (2,569 – 32,176)
4
1529.86 – 9,305.68
22.84(6.448 – 39.22 )
21,717 (3,211 – 40,224)
5
1912.325 – 11,632.1
28.54(8.060 – 49.03 )* Adjustment:δt (3℃) = 10,553, δt (4℃) = 20,963, δt (5℃) = 39,948 in Table 4 are discarded, judged as out of the range of good fitness
in the Toyama model.

Warming(℃)

Global Warming, Pollinosis, and Health Budget Impact

Health care budget impact(\ million)

25,00020,00015,00010,000
5,000
0
1

2

3

4

5

Warming (℃)

Fig. 2

Health care budget impact of cedar pollinosis
under global warming.

outpatient visit, δπ , is calculated as the product of δtand δop . The outpatient visit increases 5.7% with 1°C
warming, 11.8% with 2°C, 24.7% with 3°C, 47.4%
with 4°C and 88.2% with 5°C. The budget impact is
calculated as the product of δπ and π. It is estimated as
4,343 million yen with 1°C warming, 9,019 million
yen with 2°C, 19,209 million yen with 3°C, 37,529
million yen with 4°C and 70,675 million yen with
5°C.
Table 5 shows the budget impact with a further
assumption that the fitness of the Toyama model of air
temperature – pollen count is limited to 2°C warming,as already argued. The adjustment according to thisassumption changes the upper limit of δt (3°C), δt(4°C), and δt (5°C) into 6,979.26, 9,305.68 and
11,632.1, respectively. The increases in the outpatient
visit become 17.1% with 3°C warming, 22.8% with
4°C, and 28.5% with 5°C; and the budget impact as
13,031 million yen, 17,372 million yen and 21,717
million yen, respectively.
Figure 2 illustrates this air temperature – budget
impact relationship.

4. Discussion
Our simple combination of the available knowledge gained from our literature survey quantifies thehealth care budget impact of cedar pollinosis underglobal warming scenarios of 1°C to 5°C warming
ceteris paribus based on our health care budget impact
model. The estimated budget impact, 4,343 million
yen to 21,717 million yen, depending on the scenario,
accounts for approximately 0.016% to 0.070% of thenational health care budget of the late 1990s. The estimated relationship between the degree of warming
and the budget impact illustrates a potential impact of
global warming on the society. This broad picture provides an answer to some extent to the need for research to quantify the impacts and risks beyond qualitative analyses of consequences, which is imperative
for the development of policy against global warming,as brought up by the Global Warming Research Initiative in Japan (Koike, 2006).

77

The policy implication of this study is not prescriptive, however. There are no established rules on how
to associate health care budget impact with policy
making (Trueman et al., 2001). The long process ofglobal warming makes the interpretation of our resultsdifficult, since the health care budget impact is often
used for short- and medium-term prioritisation in
health care policy. Yet it is arguable that the budget
impact of cedar pollinosis is found to be a threat tosocial insurance based health system, which is one of
the major and excessively increasing components ofpublic expenditure controlled by the government.
Therefore, the government needs to pay attention tothis threat and consider actions and priorities to adapt
to global warming. In this sense, the information revealed by this study should be useful for policy makers to develop an adaptation strategy against global
warming.
Our estimation is so simple that it can be an oversimplification. One of the fundamental assumptions,which could undermine the meaning of our results, isour scenario of warming ceteris paribus. It is true that
global warming is a long process, during which cedar
vegetation, susceptibility of the population, medicalcare practice, etc., will transform continuously. The
assumption, “with other things being the same”, canbe an oversimplification. We make this big assumption due to the lack of plausible scenarios to date. We
also make a number of assumptions such that there isa causal relationship between pollen count and outpatient visit; that the number of outpatients with perennial allergic rhinitis during the seasons is negligible;that the co-payment ratio has been 0.3 since 1990s,
which should have been lower before 2003; that thedifferences in the definitions of variables in the
regression models are negligible; that the limited
available knowledge from various regions in various
years is sufficient to illustrate a nationwide picture,and so on. We make these assumptions due to thelimitation of the quantitative knowledge available todate. These can be an oversimplification as well.
We acknowledge that these limitations resulting
from our assumptions may make the budget impact
presented herein debatable. However, we think thetransparency of combining the available knowledge asin this study is a rigorous approach of estimation in
the context under study. We also think that our report
is a good groundwork for further study on the impactof cedar pollinosis under global warming.

Acknowledgements
This work is supported by the Global Environment
Research Fund S-4 by the Ministry of the Environment, Japan.

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M. KONDO et al.

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