Transcript Technologies for adaptation to climate change impacts on

Technologies for Adaptation to Climate
Change Impacts on Human Health
F. Agyemang-Yeboah
UNFCC SEMINAR ON THE DEVELOPMENT AND TRANSFER ON
ENVIRONMENTALLY SOUND TECHNOLOGIES FOR
ADAPTATION TO CLIMATE CHANGE, TOBAGO, 14-16 JUNE
2005
School of Medical Sciences, Kwame Nkrumah
University of Science and Technology, Kumasi,
Ghana.
Summary of Presentation
1. Potential Climate Change Impacts on
Human Health-An illustrative model
2. The Ghanaian Case Study
3. Identification of technologies from the
Ghanaian perspective
4. Other options of Adaptation
technologies
5. The decision making process
6. Conclusion
INTRODUCTION AND BACKGROUND
Potential Impacts of Climate Change on Human
Health
 It is now established that global climate change
would affect human health via pathways either
directly or indirectly at different time rate.
 Climate change act via less direct mechanism to
affect the ecosystem and therefore the
transmission of many diseases. It also affects food
security.
 The distribution and abundance of vector
organisms (carriers) and intermediate hosts are
affected by both physical (temp. humidity. rainfall
etc.) and biological factors (vegetation, host
species, competitors, predators etc.) in the
ecosystem
POOR RAINS
Inadequate in volume and distribution
Poor grass
Poor Harvest
People
Underfed
(Malnutrition)
Poverty
Animal death
Less meat, less
milk
Animals underfed
Overgrazing where
grass is good
Overgrazing,
trees cut
down for fuel
Grass /vegetation
cover lost
LAND
DEGRADATION
TYPICAL CASES FROM
GHANA
Mean air temperature scenario – seasonal
pattern
Mean air temperature baseline
33
32.1
32.1
32.1
Mean air temperature 2020
32
31.4
Mean air temperature2050
31
30.1
30.6
30
Mean air temperature
30
29.9
28.6
28.6
28.5
28.2
27.9
27
28.2
27.6
27.2
27.6
27.1
27.2
26.9
26.6
27
26.5
26.6
26.7
26.5
26.3
26.6
25.8
26
25.4
25.8
25.3
25
26.1
26
25.3
24.9
24
Jan
28.1
27.7
27.9
28.6
28.5
28.4
28
30.1
29.4
29.6
29.2
29
30.5
Mean air temperature2080
Feb
Mar
Apr
May
Jun
Jul
24.8
Aug
Sep
Oct
Nov
Dec
Distribution of the number of malaria cases and
maximum air temperature
90000
38.0
Number of Outpatient Malaria Cases
70000
60000
Feb-02
35.70C
Feb-01
35.50C
Mar-00
34.90C
36.0
Mar-03
34.90C
Jan-04
33.90C
Feb-99
33.40C
34.0
50000
32.0
40000
30000
30.0
20000
Sep-99
28.7
10000
Aug-00
28.2
Aug-01
27.6
Aug-02
28.0
Aug-03
28.4
28.0
Aug-04
27.7
0
26.0
Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03 04 04 04 04
Time (1999 - 2004)
Number of malaria cases
Maximum Temperature
High maximum air temperature values corresponds to low number
of malaria cases and vice versa.
Maximum Temperature (C)
80000
DISTRIBUTION OF MALARIA CASES AND MEAN RELATIVE HUMIDITY
75000
Jun-99
82%
65000 Apr-99
79%
55000
Jun-00
84.5%
15000
Oct-03
68043
85
80
75
Dec-00
72.5%
Dec-00
34866
Apr-99
30730
Aug-01
46039
70
Sep-99
37898
35000
25000
Jun-03
82.5%
Jun-00
56000
Jun-99
48000
45000
84.5%
Aug. 02
84.5
90
Feb-02
41979
Jul-01
40505
Feb-02
63.5%
Mar-03, 67.5
Feb-03
39700
(72%)
Feb-00
55% 18717
Feb.00
65
60
55
5000
50
YEAR (1999 - 2003)
MALARIA CASES
MEAN RELATIVE HUMIDITY
•Generally, increasing mean relative humidity corresponds to
increasing incidence of malaria, whilst decreasing mean relative
humidity corresponds to decreasing incidence of malaria.
MEAN RELATIVE HUMIDITY (%)
Sep-99
84.9%
Aug-02
80300
Aug-01
Jul-01 86%
Ja
n
M -99
ar
M -9
ay 9
Ju -99
Se l-9
9
Nop-9
9
Ja v-99
n
M -00
ar
M -0
ay 0
Ju -00
Se l-0
0
Nop-0
0
Ja v-00
n
M -01
ar
M -0
ay 1
Ju -01
Se l-0
1
Nop-0
1
Ja v-01
n
M -02
ar
M -0
ay 2
Ju -02
Se l-0
2
Nop-0
2
Ja v-02
n
M -03
ar
M -0
ay 3
Ju -03
Se l-0
3
Nop-0
3
v03
OUTPATIENT MORBIDITY MALARIA CASES
85000
90000
400
80000
350
70000
300
60000
250
50000
200
40000
150
30000
100
20000
50
10000
0
0
Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03 04 04 04 04
Time (1999 - 2004)
Number of malaria cases
Rainfall Amount
Rainfall amount (mm)
Number of Outpatient Malaria Cases
Distribution of the number of malaria cases and
rainfall amount
Comments
 Baseline study shows that under the present
climatic conditions malaria is perennial.
 Mean air temperature ranges from mean air
temperatures 24.8ºC to 27.9ºC for Ashanti
Region
 The maximum number of malaria cases occurs
in June at mean air temperature of 25.8ºC.
 Low number of malaria cases occurs in
February , March and April where mean air
temperature ranges from 27.6ºC in April ,
27.9ºC in both February and March
respectively.
Distribution of Meningitis cases and maximum air
temperature
45
38
Number of meningitis cases
35 Mar-99
33.30C
Jan-03
390C
36
Jan-03
33.1
Jan-04
33.90C
34
30
32
25
20 Mar-99
17
15
Mar-00
18
30
Feb-01
15
28
Feb-02
7
10
Jan-04
7
26
24
5
0
22
Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct- Jan- Apr- Jul- Oct99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03 04 04 04 04
Time (1999 - 2004)
Meningitis Cases
Maximum air temperature
Periods of high meningitis cases coincide with periods of high
maximum air temperature
Maximum Air temperature
Mar-00
0
34.9 C
40
Feb-02
35.70C
Feb-01
35.50C
Distribution of diarrhoea cases and rainfall
amount
9000
400
8000
350
300
6000
250
5000
200
4000
150
3000
Rainfall amount(mm)
Number of Diarrheal Cases
7000
100
2000
50
1000
0
0
Jan- Jun- Nov- Apr- Sep- Feb- Jul-01 Dec- May- Oct- Mar- Aug- Jan- Jun- Nov99
99 99 00
00
01
01 02
02 03
03
04
04 04
Time(1999 - 2004)
Diarrhoea Cases
Rainfall amount
High number of diarrhoea cases corresponds to periods of high
rainfall amount and vice versa
Direct Cost  210, 680  21, 500  4 529 620 000
Malaria
Cost of malaria treatment – 2003
figures
 Total cost = Direct Cost + Indirect Cost
 Direct Cost = Costs of Drugs + OPD fees
+ Laboratory fees etc.
 Indirect Cost = Opportunity Cost of
Labour for affected person and caring
parent
Total Cost for Children
Total Cost for Children = Direct Cost + Indirect
Cost
 Direct Cost = 210, 680 × ¢21500 = ¢4, 529, 620,
000.00
 Indirect Cost = 210680 × ¢9600× 7 = ¢ 14, 157, 696,
000.00
 Total for Children
= ¢ 18, 687, 316,
000.00
Total Cost of Adults
Total Cost for Adults (Direct + Indirect)
Direct Costs = 353608 × ¢23,000 = ¢ 8132984000.00
Indirect Cost = 353608 ×¢9,600×7 = ¢ 23762457600
Total Cost for Adults
= ¢ 31, 895, 441, 600.00
Total Cost Burden
Total Cost Burden for Adults and Children in the
year 2003
Total for Children + Total for Adults
= ¢18, 687, 316, 000.00 + ¢31, 895, 441, 600.00
= ¢50, 582, 757, 600.00 (1US$~¢8,000 at 2003
which is approximately US$6,000,000/year)
NB. Only two regions of Ghana.
Summary of socioeconomic impacts
 Reduced income of affected individuals due to
loss of productivity
 Increased expenditure of affected families
 Increased insecurity of employment (low skilled
workers and casuals)
 Diminished quality of life
 Social disruption
 Reduction in Gross Domestic Product
 Increased cost of Health Delivery at the National
Level
Population Vulnerability and Adaptive
Response
 It should however be mentioned that human
populations as with individuals, vary in their
vulnerability to certain health outcomes. This
will thus affect not only the type but the
choice of adaptive strategies to offset those
effects.
HEALTH ADAPTION STRATEGIES
FROM THE GHANAIAN PESPECTIVE
 Two main strategies can be identified
1. PREVENTIVE STRATEGY( Primary,
Secondary and Tertiary)
2. CURATIVE STRATEGIES (Diagnosis,
Management and Monitoring)
All technologies for adaptation (Ghana’s
perspective) to combat the effect of
climate change/variability on health
will be discussed along this line.
ADAPTATION OPTIONS
NB.
1-3….PREVENTIVE ADAPTATIONS
4……CURATIVE ADATATIONS
1.
PRIMARY PREVENTION: Any intervention implemented before there
is evidence of disease or injury (e.g. avoiding hazardous exposure to
asbestos, pollen, using insecticide-impregnated mosquito nets etc.)
2.
SECONDARY PREVENTION: Any intervention implemented after
disease has begun but before symptoms show (e.g. early detection or
screening for say cholera) and subsequent treatment to avert full
progression to disease. Enhancing monitoring and surveillance,
improving disaster response and recovery and strengthening the
public health system.
3.
TERTIARY PREVENTION: Any intervention to minimize the adverse
effects of an existing disease and injury (e.g. better treatment of heat
stroke, improved diagnosis of vector-borne disease.
MANAGEMENT – Any intervention taken to treat or manage existing
diseases or injury (drug prescription and compliance)
4.
HEALTH ADAPTATION STRATEGIES EXAMPLES
FROM THE GHANAIAN PESPECTIVE
DISEASE
ADAPTATION
TECHNOLOGY
TYPE OF ADAPTATION
LIMITATION/
COMMENT
MALARIA
DEVELOPMENT OF
VACCINES, HERBAL
PREPARATIONS
Introduction of Predators
to reduce vector
population
PREVENTIVE
LACK OF RESOURCES
INADQUATE FACILITIES
SKILLED PERSONNEL
MALARIA
INSECTICIDEMPREGNETED NETS
POCT, COMBINED
THERAPY
PREVENTIVE
CURATIVE
COST, INEFFICIENT
HEALTH CARE
DELIVERY SYSTEM
CSM
NEW ARCHITECTURAL
HOUSE DESIGNS, EARLY
VACCINATION, MOBILE
CLINICS, Health Education
PREVENTIVE
CURATIVE
COST
CHOLERA
BOLE-HOLE DRILLS,
ACTIVATED CHARCOAL
DOMESTIC FILTERATION
SYSTEMS. POINT OF
CARE TESTING (POCT)
PREVENTIVE
COST
ALL DISEASE
ALL DISEASES
COMPUTER INFO. SYS.
Telemedicine
PREVENTIVE
CURATIVE
COST, LACK OF
SKILLED PERSONNEL
CURATIVE
ETHICAL ISSUES
OTHER SPECIFIC ADAPTION
TECHNOLOGY OPTIONS
Heat-related illness



Design buildings to be more heat resistant (insulation, air flow, air-conditioning)
Planting trees to reduce urban heat effect
Creating public education campaigns to offset risk of heat wave
Establishing new weather watch/warning systems that focus on health related
adverse conditions
Agricultural Stresses



Production of climate- resistant seeds, plants, high yielding varieties eg.
“Obaatanpa” maize- Genetically modified grains? (ethical issues)
Promoting land reform and management systems that favour environmentally
sound land usage
Reducing the proportion of monocultural farming practices to increase yield and
also for better resistance to pests.
OTHERS- ELISA(Diagnostics), enzymes for biodegrading waste,
Affrostestation, Bushes fires, bio-fuel to reduce air pollution
APPLICATION
TECHNIQUE/
TECHNOLOGY
INDICATOR
LIMITATION/
RECOMMENTATION
Disease
Surveillance
Simple Mapping Studies
GIS and Remote Sensing
Mobile Clinics, POCT
Disease Incidence,
Prevalence
May not provide
quantitative estimate
may lack compatibility
Disease Monitoring
GIS and Remote Sensing
Telemedicine (satellite
remote sensing)
Disease Incidence,
Prevalence
May not provide
quantitative estimate
may lack
compatibility/Cost
Demographic Data/
Climate Prediction
Simple Mapping Studies
GIS and Remote Sensing
Computer Information
and Reporting Systems
Disease Incidence,
Prevalence
Lack of trained and
skilled personnel
Weather extremes
and Sea-level rise
Engineering
measures- sea walls
Flash Flood ,
residential placement
Maintenance of
disasters, reduced
erosion
Inability to provide
sufficient resources
for engineering
options
Vector-borne disease
Water-borne disease
Installation of window
screens,
Vaccination, public
education, promotion of
pyrethroid impregnated
nets, water filteration
Disease Incidence,
Prevalence
Inability to develop
pesticide and or drug
resistance products
Disease
Control/Prevention
Public Health
Education, Releae of
sterilized male insects
to reduce
reproductive capacity
Disease Incidence,
Prevalence
Insufficient resources
THE DECISION MAKING FRAMEWORK FOR
ADOPTING A TECHNOLOGY-Key Questions
(Monitoring and Evaluation)
1. What are the main drivers behind the decision? Is it mainly about
adapting to future climate? If not, could climate change be important?
Is it most appropriate to meet local needs?
2. What are the criteria for recognizing a successful outcome? What are
the legislative requirements or constraints? What are the rules for
making the decision? -risk averse or focused on maximizing benefit or
minimizing cost
3. What is the lifetime of your decision? What climate variables could be
most important? How could climate change affect your ability to meet
your objectives?
4. What range of technology options should be considered? High Tech,
Medium Tech or Low Tech?
5. How do these options rate against your criteria? Could particular
options make it difficult for others to manage climate change?
6. Is there a clear preferred option?
7. Did the decision deliver the expected benefit or not?
1
1
8
7
Identify
problem and
objectives
Monitor
2
3
Implement
decision
No
4
Identify options
Yes
Problems
defined
correctly?
6
YES
Criteria met?
Establish
decision
making criteria
Assess risk
5
Appraise options
Conclusion
 Effective technological adaptation and transfer will
require individuals skilled at recognizing, reporting
and responding to health threats associated with
climate change.
 Building capacity is therefore an essential step in
preparing adaptation strategies. Education,
awareness creation and the creation of legal
frameworks, institution and an environment that
enables people to take well-informed, long-term
sustainable decision are all needed.
 Building adaptive capacity in public health will also
require strong and determined vision of appropriate
healthcare delivery systems.
 It must be stressed adapting to climate change will
require more than financial and technology, human
resource and knowledge are essential as well as
institutions that are committed to face the health
challenges associated with climate change.
END
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