Impact of global warming
on waterborne diseases
Yasuaki HIJIOKA*, Kiyoshi TAKAHASHI*
Yuzuru MATSUOKA**, Hideo HARASAWA**NIES, Japan, **Kyoto University, JapanThe 8th AIM International Workshop
13-15, March at Ohyama Memorial Hall, NIES
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Research Objectives
z Health impact analysis on global warming

utilizing statistical modelz Focus on diarrhea, one of the waterborne

diseases,¾ Developing a diarrheal incidence estimation model(based on water supply, sanitation coverage,
present situation of diarrheal incidence, temparature)¾ Estimating the changes in diarrheal incidence infuture worlds under SRES scenario

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Present condition and causes ofwaterborne diseases


Diarrhea incidence is closely related to drinking water quality andsanitation, about 4 billion cases are reported to occur every yearwith some 2.2 million deaths （WHO）
Bern et al:• Estimated mortality from diarrhea in developing countries at 3.3
million (1.5 to 5.1 million people).
• Diarrheal incidence of 2.6 times per year per infant (aged 5 years
or younger ).
9 WHO:• Deaths resulting from diarrhea are mostly seen in infants aged 5years or younger, with diarrhea accounting for about 15% of infantmortality in developing countries¾ Necessity of improving water and sanitation condition
9z

How about is the effect of global warming for cause of diarrheal
incidence?
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Present access to water supplyand sanitation
Access to Water supply and Sanitation in 1990 and 2000
19902000

Water supply

79% (4.1billion)

82% (4.9billion)

Sanitation

55% (2.9billion)

60% (3.6billion)

But…
9 As of the year 2000 about 1.1 billion people still had noaccess to safe water
9 About 2.4 billion people were living in poor sanitaryenvironments without access to sanitation,¾ Leading to the incidence of various diseases
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Action for reduction of waterborne
diseasesz In order to improve the substandard water supply andsanitation environment, VISION 21 was initiated in 2000
under the leadership of the Water Supply and Sanitation
Collaborative Council (WSSCC).
SUGGESTED TARGETS FOR 2015 AND 2025
20152025• universal public awareness of hygiene• percentage of people who lack adequate sanitation halved• percentage of people who lack safe water halved• 80% of primary school children educated about hygiene• all schools equipped with facilities for sanitation and handwashing• diarrhoeal disease incidence reduced by 50%• fundamental requirement. Minimum sanitation standards
should be established at national level.

• good hygiene practices universallyapplied• adequate sanitation for everyone• safe water for everyone• all primary school children
educated about hygiene,• diarrhoeal disease incidence
reduced by 80%.

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Impact of global warming onwaterborne diseases
 Temperature are also strongly related towaterborne diseases, and global warming is
forecast to further increase the incidence ofwaterborne diseases
9 Checkley et al. reported that a temperature increase of 1℃ would

lead to an 8% rise in the number of diarrhea cases (aged 10years or younger in Peru)
9 Singh et al. reported a positive correlation between annual

average temperature and diarrheal incidence as well as anegative correlation between water supply accessibility anddiarrheal incidence in the Pacific Islands

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Estimation method
z Region: Regional categories of the Global Burden of Diseases
2000 project (GBD) conducted by WHO
9 GBD divides the world into 14 regions based on geographicaldistribution and mortality rates,
9 Estimates the incidence, mortality, etc. of various diseases
according to these regions

z Estimation: Multiple regression analysis on diarrheal incidence(diarrheal incidence per capita per year) in 2000 (present condition)
reported by GBD by region and factors related to incidence, andestimated future diarrheal incidence （2000，2025，2055）.

z Parameter: Four factors related to diarrheal incidence: watersupply coverage, sanitation coverage, annual average temperature,and per capita GDP.

z Scenario: SRES (A1B, A2, B1, B2)
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Regional reporting categories for Global Burden of Disease 2000 project:
WHO regions and 14 subregions
Index

WHOregion

Mortality
strarum

WHO Member States

AFRO_D

AFRO

D

Algeria, Angola, Benin, Burkina Faso, Cameroon, Cape Verde, Chad, Comoros, Equatorial Guinea, Gabon, Gambia,Ghana, Guinea, Guinea-Bissau, Liberia, Madagascar, Mali, Mauritania, Mauritius, Niger, Nigeria, Sao Tome AndPrincipe, Senegal, Seychelles, Sierra Leone, Togo

AFRO_E

AFRO

E

Botswana, Burundi, Central African Republic, Congo, Côte d'Ivoire, Democratic Republic Of The Congo, Eritrea,Ethiopia, Kenya, Lesotho, Malawi, Mozambique, Namibia, Rwanda, South Africa, Swaziland, Uganda, United
Republic of Tanzania, Zambia, Zimbabwe

AMRO_A

AMRO

A

Canada, United States Of America, Cuba

AMRO_B

AMRO

B

Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Brazil, Chile, Colombia, Costa Rica, Dominica,Dominican Republic, El Salvador, Grenada, Guyana, Honduras, Jamaica, Mexico, Panama, Paraguay, Saint Kitts AndNevis, Saint Lucia, Saint Vincent And The Grenadines, Suriname, Trinidad And Tobago, Uruguay, Venezuela

AMRO_D

AMRO

DBolivia, Ecuador, Guatemala, Haiti, Nicaragua, Peru

EMRO_B

EMRO

BBahrain, Cyprus, Iran (Islamic Republic of), Jordan, Kuwait, Lebanon, Libyan Arab Jamahiriya, Oman, Qatar, Saudi
Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates

EMRO_D

EMRO

D

Afghanistan, Djibouti, Egypt, Iraq, Morocco, Pakistan, Somalia, Sudan, Yemen

EURO_AEUROAAndorra, Austria, Belgium, Croatia, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland,Israel, Italy, Luxembourg, Malta, Monaco, Netherlands, Norway, Portugal, San Marino, Slovenia, Spain, Sweden,Switzerland, United Kingdom

EURO_BEURO

EURO_C

BAlbania, Armenia, Azerbaijan, Bosnia And Herzegovina, Bulgaria, Georgia, Kyrgyzstan, Poland, Romania, Slovakia,Tajikistan, The Former Yugoslav Republic Of Macedonia, Turkey, Turkmenistan, Uzbekistan, Yugoslavia

EUROCBelarus, Estonia, Hungary, Kazakhstan, Latvia, Lithuania, Republic of Moldova, Russian Federation, Ukraine

SEARO_B

SEARO

B

Indonesia, Sri Lanka, Thailand

SEARO_DSEARO

DBangladesh, Bhutan, Democratic People's Republic of Korea, India, Maldives, Myanmar, Nepal

WRPO_AWPROAAustralia, Japan, Brunei Darussalam, New Zealand, Singapore

WPRO_B

WPRO

B

Cambodia, China, Lao People's Democratic Republic, Malaysia, Mongolia, Philippines, Republic Of Korea, VietNam, Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia (Federated States Of), Nauru, Niue, Palau, Papua
New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu
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Selection of Explanatory Variables(Multiple Regression Analysis)
A: SUPPLY, B: SANITATION, C: TEMP, D: LN（GDP/CAP）
A

B

C

D

AB

AC

AC’

AD

BC

BD

CD

ABC

ABD

ACD

BCD

ABCD

0.62

0.64

0.56

0.69

0.72

0.74

0.85

0.71

0.70

0.71

0.76

0.77

0.73

0.77

0.75

0.76

0.44

0.43

0.47

0.40

0.38

0.36

0.29

0.38

0.39

0.38

0.35

0.34

0.37

0.34

0.35

0.35

Number of Determinant1.00

1.00

1.00

1.00

2.00

2.00

2.00

2.00

2.00

2.00

2.00

3.00

3.00

3.00

3.00

4.00

Intercept (a)

2.63

1.32

-25.744.71

2.38-13.38 -19.05

4.35-12.46

3.66

-9.41

-9.13

3.50

-8.50

-7.88

-7.31

SUPPLY (b)

-3.89

-2.19

-2.58

-1.73

-1.87

-1.48

-1.43

-0.98

-1.01

Coefficient

Squared multiple correlation
coefficient adjusted for thedegrees of freedom
Standard error

SANITATION-2.58

TEMP (c)

-1.55
0.09

t-value

LN(GDP/CAP)
3.74

SUPPLY-4.67

TEMPLN(GDP/CAP)

-1.70
0.05

3.20

-4.32

4.79

-4.86

-0.41
3.90

-2.20

-3.62

4.46

-2.16

-3.07

-3.39

-1.44

-2.34
4.22
-5.44

-1.75

-2.59
2.64

-1.19

0.05

0.07

-0.62

Intercept

SANITAION

-2.29

0.04
-0.39

-0.43

3.06

-1.41

-1.44

1.94

4.15
-2.25

2.13
-2.06

-3.27

-1.32
-0.72

-0.59

0.04

0.04

0.03

-0.25

-0.28

-0.33

-0.20

-1.40

2.99

-1.31

-1.13

-1.07

-1.97

-1.25

-1.33

-1.42

-1.24

0.04

1.76
-1.12

-1.19
-0.88

-0.74

2.00

1.68

1.60

-1.58

-1.80

-0.96

A: SUPPLY, B: SANITATION, C: TEMP, D: LN（GDP/CAP）

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Procedure for diarrhea incidence estimation
SRES scenarios
Multiple Regression Analysis

A1B:Moderate
A2:Environmental
environmental preservation preservation-neglecting(rapid economic growth) (slow economic growth)

Independent
variables
Dependent
variable

SUPPLY

LN（INC/CAP）SANITATION

B1:Environmental
preservation-oriented(rapid economic growthsecond to that of A1B)TEMP

B2:Environmental
preservation-oriented(moderate economicgrowth)

LN(GDP/CAP)

Future scenarios

Diarrhea incidence estimationln( INC / CAP ) = d × e

a +b×SUP + c×TEMP

INC/CAP：Diarrheal incidence (incidence peryear per capita)，
SUP：Water supply coverage (%/100)，
TEMP：Annual temperature average (K)，a, b, c： regression coefficient ，d： correction
coefficient
Diarrheal incidence of about 7.3% per 1℃
increase in temperature（1）

Annual average temperature•AO-GCM (NIES/CCSR, IS92a)•Simplified climate model•Scaling methodWater supply coverage estimation
SUP = Min (1, c × (a + b × ln(GDP / CAP )))GDP/CAP：per capita GDP（PPP，1999US$），a, b：regression coefficient，c：correction coefficient
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Changes in diarrheal incidence by region80

GBD Region

60z

40zz

SEARO_D

WPRO_B

A2
WPRO_A

A1B
B2
EURO_C

EURO_A

EMRO_B

AMRO_D

AMRO_B

AMRO_A

AFRO_E0

EMRO_D2000
B1

EURO_B

20

AFRO_D

Coverage of water supply [%]1000.0
WPRO_B0

WPRO_A

00.3

SEARO_D

1EURO_C

5EURO_B

2

EURO_A

10A20.6

EMRO_D3

EMRO_B

15A1B
B20.9

AMRO_D

4AMRO_B

20

1.2

AMRO_A

52000
B1

AFRO_E25

1.5

AFRO_D

6Diarrheal incidence per capita per year

30

Increase of temperature [ ℃ ]Annual average temperature [℃ ]（GroupⅠ:AMRO_A, EURO_A, EURO_C, WPRO_A ）Present water supply coverage:100％
Low level (0.2 to 0.25) in 2055
Almost the same as the present
condition, although there are slightdifferences according to the level oftemperature increase among thescenarios

GBD Region
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Changes in diarrheal incidence by region80

GBD Region

60z

40z

GBD Region

SEARO_Dz
WPRO_B

A2
WPRO_A

A1B
B2
EURO_C

EURO_A

EMRO_B

AMRO_D

AMRO_B

AMRO_A

AFRO_E0

EMRO_D2000
B1

EURO_B

20

AFRO_D

Coverage of water supply [%]1000.0
WPRO_B0

WPRO_A

00.3

SEARO_D

1

EURO_C

5

EURO_B

2

EURO_A10

A20.6

EMRO_D3

EMRO_B

15

A1B
B20.9

AMRO_D4

AMRO_B20

1.2

AMRO_A52000
B1

AFRO_E25

1.5

AFRO_D

6

Diarrheal incidence per capita per year

30

Increase of temperature [ ℃ ]Annual average temperature [℃ ]（GroupⅡ:AMRO_B, EMRO_B, SEARO_D ）Present water supply coverage: over 85％
Water supply coverage reaches 100% in
2055 for all scenarios excluding A2
Differences in the incidence are seenreflecting the temperature differencesamong the regions with relatively highaverage temperatures, so that theincidence does not decrease to the levelof Group I
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Changes in diarrheal incidence by region80

GBD Region

60z

40z

GBD Region

SEARO_D

WPRO_B

A2
WPRO_A

A1B
B2
EURO_C

EURO_A

EMRO_B

AMRO_D

AMRO_B

AMRO_A

AFRO_E0

EMRO_D2000
B1

EURO_B

20

AFRO_D

Coverage of water supply [%]1000.0
WPRO_B0

WPRO_A

0

0.3

SEARO_D

1

EURO_C

5

EURO_B

2

EURO_A10

A20.6

EMRO_D3

EMRO_B

15

A1B
B20.9

AMRO_D4

AMRO_B20

1.2

AMRO_A

52000
B1

AFRO_E25

1.5

AFRO_D

6

Diarrheal incidence per capita per year

30

Increase of temperature [ ℃ ]Annual average temperature [℃ ]（GroupⅢ:AFRO_D, AFRO_E, AMRO_D, EMRO_D, EURO_B, WPRO_B ）zPresent water supply coverage: under 85％
Large differences among scenarios in thediarrheal incidence in 2055 with the
exception of EURO_B
The highest diarrheal incidence in 2055 isscenario A2 in the AFRO_D region (1.18),
showing little improvement from the presentcondition.
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Changes in diarrheal incidence by scenario（AFRO_D）

Differences in temperature increase(A1B: 2.45℃, B1: 1.63℃)¾

Diarrheal incidence in B1 in 2055 is11% lower than in A1B

1.2

4.0
INC/CAP(A1B)
INC/CAP(Baseline)
SUPPLY
TEMP

0.9

2.4

0.6

1.6

0.3

0.8

0.0
2000

2025

Baseline: Only water supply coveragechanges while the temperature does notchange
9 Differences (2025,2050):
A1B(0.07, 0.10), B1(0.04,0.06)¾ Influence of temperature
increases is small

Diarrheal incident per capita per year,Coverage of water supply [×100%]

1.5z

A1B
3.21.20.02055
4.0

INC/CAP(B1)
INC/CAP(Baseline)
SUPPLYTEMPB1
3.2

0.9

2.4

0.6

1.6

0.3

0.8

0.0
2000

Temperature [ ℃ ]zWater supply coverage in 2055 reaches
100% in both scenarios

1.52025

Temperature [ ℃ ]z

AFRO_D: Highest diarrheal incidence in2000 (1.23) and exhibited a large
difference among the scenarios

Diarrheal incident per capita per year,
Coverage of water supply [×100%]

0.02055

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Changes in diarrheal incidence by scenario（AFRO_D）z

B2
Temperature increase is small
9 Water supply coverage remains at
81% in 2055
9 Diarrheal incidence is lower than in
A2 and higher than in A1B and B1
and B19Differences (2025,2050):
A1B(0.12, 0.23), B1(0.14, 0.27)
9Temperature increases exert asignificant influence

1.2

3.2

0.9

2.4

0.6

1.6

0.3

INC/CAP(A2)0.8
INC/CAP(Baseline)
SUPPLY
TEMP
0.02055
2025

A20.02000
1.5
Diarrheal incident per capita per year,Coverage of water supply [×100%]z4.0

INC/CAP(B2)
INC/CAP(Baseline)
SUPPLY
TEMP

4.0

B2

1.2

3.2

0.9

2.4

0.6

1.6

0.3

0.8

0.0
2000

2025

Temperature [ ℃ ]Temperature increase is large
9 Water supply coverage is low
9 Diarrheal incidence shows
insignificant decreases : 1.23(2000),
1.21(2025), 1.18(2055)
91.5

Temperature [ ℃ ]A2Diarrheal incident per capita per year,
Coverage of water supply [×100%]

z0.0
2055

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60500.8
INC/CAP
INC/CAP(Baseline)
0.6

0.4

0.22025

B2

B1A2A1B

0.0
B2

2055：A1B（0.39），B1（0.35），
B2（0.40），B1, the scenario withthe lowest level (about 50%
reduction compared with 2000)
9 A2： Level of 0.54 even in 2055,
remaining at the 2025 level ofthe other scenarios
970

B1

Diarrheal incidence

80A2z90

A1B

Under 100% in any scenario in2025
9 In 2055, B1(100%), A1B(98%),
A2(87%), and B2(95%)
91002000

Water Supply Coverage

Diarrheal incidence per capita per yearz

Coverage of water supply [%]Changes in water supply coverage anddiarrheal incidence from a global perspective2055

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Conclusionz

From the results of multiple regression analysis, we haveproposed a diarrheal incidence estimation equation withtemperature and water supply coverage as predictor
variables.

z

In addition to the regional differences in present
temperature and water supply coverage, large
differences in the decrease of diarrheal incidence in 2055
are estimated among the regions due to changes inthese factors in the future scenarios.

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Conclusion continued
zThe results of our analysis based on four SRES
scenarios show that in order to reduce diarrheal
incidence, it is important to give consideration to theenvironment concurrently with socioeconomic growth.
Moreover, in order to reduce diarrheal incidence more
rapidly, the conventional level of water supply coveragepolicy is found to be insufficient when considering
environmental degradation resulting from climate change,
and it is suggested that more progressive policies
according to the actual realities of the regions arenecessary.

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