Transcript Diapositiva 1

Training on Vulnerability and
Adaptation Assessment for the Latin
America and the Caribbean Region
HUMAN HEALTH SECTOR
Paulo Lázaro Ortíz Bultó, PhD
Climate Center-Meteorological Institute. Cuba
Email:[email protected]
or [email protected]
Goals of training

An approach and methods needs to increase our
understanding of the issue of climate variability, climate
change and health assessment.

A general discussion on the potential impacts of climate
variability and change on health sector in the region.

A general discussion about of steps in a vulnerability and
adaptation assessment.

Provides concepts and examples of coping and adaptive
capacity in the region.

A general discussion about the data, tools and methods
available to assess V&A in the health sector by means of
a case of study.
Human health vulnerability to climate can
be defined as a function of :



Sensitivity, which includes the extent to health, or the
natural or social systems on which health outcomes
depend of sensitive to changes in weather and climate
(the exposure–response relationship) the characteristics
of the population, such as its demographic structure.
The exposure the climate-related hazard, including the
character, magnitude, and rate of climate variation.
The adaptation measures and actions in place to
reduce the burden of a specific adverse health outcome
(the adaptation baseline), the effectiveness of which may
influence the exposure–response relationship.
Health as an integrating issue in climate
variability and climate change
Climate variability and change
Disasters
Human
Health
Water Resources
Agriculture & Food Security
Energy & Built Environment
Corvalán, C., 2006
Climate variability influences human
Health, three way interconnected
Distribution and quality
of water
Life cycle of disease vectors
and host/vector relationships
Ecosystem dynamics of
predator/prey relationships
Pathways from Driving Forces to
Potential Health Impacts
Corvalan et al., 2003
Steps in the Vulnerability and Adaptation
Assessment in health sector (Kovasts et, al 2003)

Step 1.

Step 2. Describe the current distribution and burden of climatesensitive diseases.





Determine the scope of the assessment.
Step 3. Identify and describe current strategies, policies and
measures which reduce the burden of climate-sensitive
diseases.
Step 4. Review the health implications of the potential impact of
climate variability and change in other sectors.
Step 5. Estimate the potential health impact using scenarios of
future climate change, population growth and other factors for
describe the uncertainties.
Step 6. Synthesize the results and draft a scientific assessment
report.
Step 7. Identify additional adaptation policies and measures to
reduce potential negative health effects, including procedures
for evaluation after implementation.
Step 1: Include to Identify Indicators in
Sectors and Examine Current Conditions.

Key sectors
– Solicit or survey local decision-makers and stakeholders
– Is appropriate rank or set priorities according to climate
sensitivity and importance
– Define baseline conditions using current data related to
sectors and indicators
Step 1:
(cont’d)
Some Indicators of impacts






Increased disease incidence
Increased disease prevalence
New records of disease
Severe forms of diseases
Increased case fatality rate
Cases exceed medical capacity
Demography

population, age structure, migration index
Step 2: Include to description the current
burden and recent trend in the incidence
and prevalence of climate-sensitive health
determinant and
develop Baseline
Scenarios (without climate change)
Examine recent trends and seasonal variation and
the relationship climate variables, including:


Identification the signal climate in the patterns diseases.
To analyze association with exposure to weather or climate
variability.
Step 3: Include the key aspects to
address for specific health outcome
The specifics questions include the following:



What is being done now to reduce the burden of disease?. How
effective are these policies and measures?
What could be done now to reduce current vulnerability?. What
are the main barriers to implementation (such as technology or
political will)?
What options should begin implemented to increase the range of
possible future interventions
Step 4: Include the results of other
assessments should be includes to
better understand.
Sectors such as:

Agriculture and food supply, water resources, disasters on coastal
and river flooding.

Review the feedback from changes in population health status in
these sectors.
Step 5: Requires the generation and
using climate scenarios. Climate
scenarios are now available for a range
of time scales.
Examine different


:
Models of climate change should include projections as other
relevant factors may change in the future, such as population
growth, and other relevant factors.
The potential future impact of climate variability and change on
health may be estimated using a variety of methods.
Step 6:
This step synthesizes the quantitative
and qualitative information collected in the
previous steps.
Includes :



to identify changes in risk patterns and opportunities.
to identify links between sectors, vulnerable groups and
stakeholder responses.
Convening an interdisciplinary panel of experts with relevant
expertise is one approach to developing a consensus assessment.
Step 7: Identify possible adaptation measures that
could be undertaken over the short and long term.
Goals of this step


To increase the capacity of individuals communities and
countries to effectively cope with the weather exposure of
concern.
To identify possible measures can be taken today and in the
future to increase the ability of individuals communities, and
institutions to effectively cope with future climate exposure.
Some Climate Trends
Observed
Climate Change May Entail Changes in Variance, as
Well as Changes in Mean
Climate change and ENSO event frequency distribution. Sea surface
temperature Anomalies (SSTA) in the region Niño 3 about scenarios
without and with climate change)
Trend
Without climate change
with climate change
Frequency distribution
Trend Anomaly temperatures in the north and south
hemisphere (1860-1999)
North hemisphere
South hemisphere
Main Climate Trends Observed in Cuba During the 1990s

Increase in mean environmental air temperature, primarily
due to increases in minimum temperature

Decrease in diurnal variation temperature (Oscillation)

Increase in precipitation in the dry season and decrease in
the wet season

Later start of the wet and dry seasons, and a lag in the
summer precipitation

Increase in extreme weather events: e.g. droughts, floods,
and other dangerous meteorological events

Stronger hurricane seasons

More frequent extreme temperature events [warm events
(1991-1993, 1994-1995, 1997-1998, 2002-2003) and cold
events (1994, 1996, 1998-1999, 1999-2000)]
Research in multiples scale and data in Health Sector



Research: Is need to conduct community based
assessments and systematic research on the issues of
climate change impacts in our countries and in all region.
Multiples Scale: Local, regional and national scales are
interconnected in supporting and facilitating action on
climate change, is need for data at multiple scales and
research that links scales to understand these relationships.
The Data: Innovative approaches to health and climate
assessment are needed and should consider the role of
socio-cultural diversity present among countries. This
requires both qualitative and quantitative data, and the
collection of long term data sets on standard health
outcomes at comparable temporal and spatial scales. They
favor the development appropriate applications for the
sector health.
How are the relationships between variability and climate change
and epidemiological pattern changes?
Variability and Climate Change
Changes in the
biological transmition
. Dynamics of the vector
.Dynamics of the pathogens
Socio-Economic
Change
Ecological Change
. Biodiversity Loss
•Migration
•Famine
•Sanitation
•Population
. Communityre location
. Nutrient cycle changes
Malaria
Yellow fever
Epidemiological Change
Dengue
Vector-Borne diseases
or not
Meningococcal
meningitis
Filariasis
ARIs
ADDs
Hepatitis
Others
Methods
Research methods used so far include
predictive modelling, analogue methods and
early effects.
Predictive models include
biological models (e,g malaria), empirical
statistical models (e.g, temperature-mortality
relationships), the used the complex index
simulation variability climate change and other
processes (e.g, relationship climate index and
diseases) and integrated assessment (IA)
models. Is need the balance empirical analysis
with scenario-based methods and to integrate
the different methods through, for example, IA
methods. The outcome of an assessment may
not necessarily be quantitative for to be useful
to stakeholders.
Simulation of impacts with the
vectorial capacity model
Parameters of the vectorial
capacity
V:
vectorial capacity is the daily rate at which
p:
n:
Probability of the vector surviving through 1 day
The parasite extrinsic incubation period in the
future inoculations arise from an infective
member of a non-immune community.
Ma: Composite index of the daily manbiting rate
a : Daily man biting habit is obtained from
vector
Expression to Malaria epidemic risk
calculation
ER 
i
i
T R
m
m
T R
x 100
Expression to epidemic risk calculation from
models on climate and health used in Cuba
c
I
1  a
c
I 
1  a
1
0
1
k
i 1
i
i 1
c
c
I 
1  a
0
m
Ortíz et al., 2001
k
2
1
k
i 1
i
i
Some diseases of
Climate Sensibility
High priority diseases identified in Brazil
Cities
diseases
Study Periods
Dengue fever
Jan, 1988 – Dec, 2002
Leptospirosis
Jan, 1988 – Dec, 2002
Meningococcal
Meningitis
Jan, 1988 – Dec, 2002
Recife
Dengue fever
Jan, 1995 –Dec, 2002
Marabá
Malaria
Jan, 1992 – Dec, 2002
Rio de Janeiro
The high priority diseases identified in the
small island states.



Disease
Identified:
malaria,
dengue,
diarrhoeal
disease/typhoid, heat stress, skin diseases, acute
respiratory infections, viral hepatitis, varicella (Chicken
pox), meningococcal disease and asthma, toxins in fish
and malnutrition.
The possibility of dust-associated diseases with the
annual atmospheric transport of African dust across the
Atlantic, is unique to the Caribbean islands.
In addition to weather and climate factors, social aspects
such as culture and traditions are important in disease
prevalence.
Ebi, et al., 2005 and Ortíz, 2004, 2006
Many different types of uncertainty relate to the health
effects of climate change
Source of uncertainty
Problems with data
Examples
1.
2.
3.
1.
2.
3.
Problems with models (relationships
between climate and health)
4.
1.
2.
Other sources of uncertainty
3.
4.
Missing components or errors in data
“Noise” in data associated with bias or incomplete
observations
Random sampling error and biases in a sample.
Known processes but unknown functional relationships or
errors in structure of model
Known structure but unknown or erroneous values of some
important parameters.
Known historical data and model structure but reasons to
believe that the parameters or model or the relationship
between climate and health will change over time.
Uncertainty introduced by approximating or simplifying
relationships within the model.
Ambiguously defined concepts or terms
Inappropriate spatial or temporal units (such as in data on
exposure to climate or weather)
Inappropriateness of or lack of confidence in the underlying
assumptions
Uncertainty resulting from projections of human behaviour
(such as future disease patterns or technological change)
in contrast to uncertainty resulting from “natural” sources
(such as climate sensitivity)
Kovats et al., 2003
Case Study: Cuba
Indicators used in the study
Global Data:
For each month include three variables.
Multivariate ENSO Index, (MEI) Quasi-Biennial
Oscillation,
(QBO)
and
North
Atlantic
Oscillation, (NAO) values available prior to 1950
of Climate Diagnostic Center (CDC). These
indices can be considered as an expression of
the forcing of the interannual, decadal variability
in the studies region.
Epidemiological data:
Thesis base include the indicator of the number of
cases the: acute respiratory infections (ARIs),
acute diarrhoeal disease (ADDs), viral hepatitis
(VH), varicella (V), meningococcal disease (MD)
and malaria borne Plasmodium falciparum and
Plasmodium vivax.
Ecological data:
Climatic data.
These base include series of monthly from
maximum and minimum temperature in 0C,(XT,
NT) precipitation in mm, (PP) atmospheric
pressure in hPa, (AP) water vapor pressure in mm
of Hg, (VP) relative humidity in %, (RH) thermal
oscillation, (TO) day with precipitation, (DP) solar
radiation in MJ/m2, (SL) and insolation in HL, (I)
were available for 51 stations in all country. For
the period 1961-1990 that constitute baseline
climate, and 1991 to 2003 is used for the
evaluated to conditional actuality.
The base date ecological includes the following
indicators: Larval density (LD) and biting density
hour (BDH), as indicative entomological we use
the number of positive houses (NPH).
Socio-economic data:
In this case used variables such as % of
residences without potable water (PHD); % of
residences with soil floors (PHF); illiteracy rate
(IR); monthly births (MB); and index of monthly
infestation (IMI).
To define climate characteristics and its health effects in Cuba,
a complex approach has been developed
Include
Maximum and Minimum
Temperatures
•Daily Oscillation Temperatures
•Relative Humidity
•Vapor pressure
•Atmospheric pressure
•Rainfall
•ENSO influence (MEI)
Determinate
by
EOF
CLIMATE
INDEXES
(IB1,IB2,..)
In Cuba:
IB1
Warm, dry,
not rainy
Winter
IB2
(Empirical
Orthogonal
Functions)
Describes the seasonal climate patterns
 - 2 ................ IB1 ...........  + 2
Transition
seasons
Hot, humid,
rainy
Summer
They
explain
about 80%
of the total
climate
variance
Describes the intraseasonal climate patterns
(Ortíz et al., 1998, 2001)
Expression to anomalies in the different scales of
the variability calculation.


 ,t   


IBt ,r , p 1     
n
IB t,r,p: the Bultó Index, expresses the climate variability (CV) at
time t, in region r, in the country p
where:
:
describe the CV that characterize the study region
: weight for each variable
,t: series of weather and CV at time t
: mean value of the weather and CV
: standard deviation of the variable
Ortíz et al., 2006
Interpretation of the indices.




IBt,1,c describes inter-monthly and inter-seasonal
variation; Includes maximum and minimum mean
temperature, precipitation, atmospheric pressure,
vapor pressure, and relative humidity.
IBt,2,c
describes
seasonal
and
inter-annual
variation; Includes solar radiation and sunshine
duration as factors that affect temperature and
humidity. Positive values are associated with a high
solar energy level.
IBt,3,c describes inter-annual and decadal scale
variation and includes the same climate variables as
IBt,1,c
IBt,4,c
describes
the
relationships
among
socioeconomic variables and can be interpreted as
life quality, or the degree of poverty as their
influence disease risk.
Behavior of the ranges by months to determine the level
risk climate of the variation according to the IB t,3C.
22
20
20
18
18
16
Moderate risk
16
14
14
12
12
10
Low risk
10
8
6
8
6
Moderate risk
4
4
2
2
High risk
Year
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
0
1982
0
1981
Range
22
High risk
Ortíz, et al., 2006
Some diseases of
Climate Sensibility
Association between climate variability and viral
hepatitis according to the indexes
Area High Risk
Area Low Risk
Ortíz, et al., 2006
149
237
325
413
502
590
678
766
855
949
above
Association between climate variability and acute
diarrhoeal disease according to the indexes
Area High Risk
Area Low Risk
Ortíz, et al., 2006
5126
10252
15378
20503
25629
30755
35881
41007
46133
51258
abov e
Association between climate variability and the number
of positive houses (hotspot) of the Aedes aegypti by
climate variability according to indexes
Area High Risk
Area Low Risk
Ortíz, et al., 2006
341
682
1024
1365
1706
2048
2389
2730
3072
3413
above
Association between climate variability and the
Meningitis a Neumococo according to the indices.
Area High Risk
Area Low Risk
Ortíz, et al., 2006
0.045
0.591
1.136
1.682
2.227
2.773
3.318
3.864
4.409
4.955
above
Spatial - Temporal
Distribution of some
diseases according to
climate index for Cuba.
Behavior of the Varicella (chicken pox)
according to I-Moran
23
-0.1
-0.1
1.65
0.4
1.16
0.2
-0.1
0.9
0.7
Latitud
22
0.67
0.19
-0.5
21
-0.8
-0.30
.3
-0
-0.79
-82
-80
-78
Longitud
-76
Behavior of the ADDs according to I-Moran
23
-0
.3
-0.2
-0.2
-0.2
0.1
0.03
Latitud
.2
-0
.1
-0
0
-0.
0.1
.2
-0
.2
-0
-0.1
-0.1
22
0.12
-0
.3
-0.06
.1
-0
0.0
-0.15
21
-0.24
0.1
-0.33
-82
-80
-78
Longitud
-76
Behavior of the VH according to
I-Moran
23
0.3
0.46
-0.8
0.5
0.11
22
Latitud
6
-0.
-0.2
-0.1
0.1
0.1
0.3
-0.2
.4
-0
-0.1
-0.25
-0.60
21
-0.96
-1.31
-82
-80
-78
Longitud
-76
Distribution time - spatial of IBt,3,c
23.5
23.5
23.5
23.5
22.5
22.5
22.5
22.5
21.5
21.5
21.5
21.5
20.5
20.5
20.5
20.5
19.5
19.5
19.5
19.5
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
LAT
Jan
Feb
Mar
Apr
23.5
23.5
23.5
23.5
22.5
22.5
22.5
22.5
21.5
21.5
21.5
21.5
20.5
20.5
20.5
20.5
19.5
19.5
19.5
19.5
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
May
Jun
Jul
Aug
23.5
23.5
23.5
23.5
22.5
22.5
22.5
22.5
21.5
21.5
21.5
21.5
20.5
20.5
20.5
20.5
19.5
19.5
19.5
19.5
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
-86 -82 -78 -74 -70
Sep
Oct
Nov
LONG
Dec
-1.309
-1.018
-0.727
-0.436
-0.145
0.145
0.436
0.727
1.018
1.309
above
Climate Change
Scenarios.
Estimate Potential Future
Health Impacts




Requires using climate scenarios
Can use top-down or bottom-up
approaches
– Models can be complex spatial models
or be based on a simple exposureresponse relationship
Should include projections of how other
relevant factors may change
Uncertainty must be addressed explicitly
Kovats et al., 2003
Estimate Potential Future
Health Impacts
In our case are used:


Scenarios of Climate change (and other
changes) are used as inputs into a model on
climate and health.
Models spatial combination with models
Generalised
Autoregressive
Conditional
Heteroskedasticity (GARCH) with dummy
variable for the model on climate and health.
Ortíz et al., 2004, 2006
MACVAH/AREEC Model


Model MACVAH/AREEC (Model of the Anomaly
Variability and Climate Change Impact on Human
Health- Assessment Risk Epidemic and Costs
Estimate).
This Model describes the Anomaly Climate
variability and Change for the impact on the
Human Health used as input the scenarios
output of climate change and health models
proposes for diseases, generating maps of risk
epidemic for Cuba using GIS. Finally, were
estimated the impact of Costs to variability and
change. The spatial correlation explains for each
disease the capacity to dissemination of the
epidemic and the range of the correlation
describes the trend epidemic.
Ortíz,2004
Climatic change scenarios.
Ortíz, et al., 2006
Scenario of variability climate the Low sensibility (Rates of
change per decade) with climate variability sensitivity the in
the range < 0.70
-8 4
-8 2
-8 0
-7 8
-7 6
26
26
Ortíz, et al., 2006
24
24
24
N
22
22
20
20
20
24
20
IB1,t,c - L
0.42 - 0.49
0.50 - 0.59
0.60 - 0.69
100
0
100
200 Kilometers
18
18
-8 4
-8 2
-8 0
-7 8
-7 6
Scenario of variability climate the high sensibility.
(Rates of change per decade) with climate variability
sensitivity in the range > 0.70
-8 4
-8 2
-8 0
-7 8
-7 6
26
26
Ortíz, et al., 2006
24
24
24
N
22
22
20
20
24
20
20
IB1,t,c - H
1.01 - 1.05
1.06- 1.11
1.12 - 1.38
100
0
100
200 Kilometers
18
18
-8 4
-8 2
-8 0
-7 8
-7 6
Potential impact according to scenarios in Cuba.
Trend
Diseases
Effects
_
Bronquial Asthma
Decrease of the number of
cases in winter
++
Acute Respiratory infection
A new epidemic peack on
warm season
Transmission way
Air-borne diseases
Effects of high
climate variability
IBt,1,C
+
Meningococcal diseases
Increase of incidence in
winter season
++
Chicken pox
Advance of the epidemic
outbreak
++
Viral hepatitis
Increase of the incidence
in winter season
++
Acute diarrhoeal diseases
Advance the increase of
incidence to winter
months
++
Dengue fever
More frequent epidemic
outbreaks and change of
seasonal patron and spatial
Water-food borne diseases
Vector Borne Diseases
Ortíz et. al., 2006
Economic impact on Human
Health due to variability and
climate change.
Climate - Health Group.
PNCT Project-Cuba
Estimate health cost ( millions US$) associated with
climate variability. Jan/2001-Mar/2002.
Cost of
hospitalization
Restricted
activity day
Diseases
Cost of
Attention
HV
ADDs
Dengue
Fever
Meningitis
by
Neumoco*
8 874.06
373 073.6
-
8 657.10
175 067.95
-
917 50.0
547 059.2
-
-
231 318.00
-
Total Cost
Treatment
cost
Cost of
Total Cost
Service
of
Urgency
5 505.0
1 236.79 116 022.95
76 064.6
36 463.4 1 207 728.75
3 745 605.66
3 745 605.66
-
231 318.00
5 300 675.36
* All cases of admission in hospitals.
Ortíz et, al,. 2004
Economic Cost (million US$).according to scenarios 2010.
Diseases
Cost of
IC
AD
Cost AD
Total Cost
IC
ARIs
329 976
43 2021.44
98 993
33 775 21.87
77 477 442.87
ADDs
136 423
18 067 862
40 927
7 994 680.18
26 06542.18
VH
10 860
1 438 298
3 258
1 937 109.06
3 375 407.06
V
19 200
25 42 848
-
-
2 542 848.00
MD
3 196
-
3 196
2 556 800.00
2 556 800.00
MD *
11 523
-
11 523
9 218 400.00
9 218 400.00
* With epidemic
IC: Increase of cases
General Cost 121 233 440.11
AD: Cases of admission in hospitals
Ortíz el at. 2004
Adaptation
measures
Climate - Health Group.
SGP-037. Project-IAI
Some examples of adaptation measures to climate
variability and change in Cuba. (Ortíz, el al 2006)
Options of adaptation
Current activities
Future activities
To strengthen primary
health care of the public
health system.
Health promotion and preventive
activities in health by means of
specific programs reduce the
population vulnerability. Education
programs according to environment
risks
including
change
and
variability of the climate and theirs
effects on human health. Increase
the use of vaccines against some
community diseases.
To continue developing the
programs of Health promotion
and
preventive
programs
increasing
the
community
participation on health.
Increasing the participation of the
local governments and others
sectors in developing the best
conditions of life in order to
guarantee the sustainability of
human health.
Measures to improve the
surveillance system in
health.
To maintain the forecast of the main
communities diseases with a good
information at all levels of the
National Public Health System
Increasing an early warning system
to predict epidemics.
To continue developing researches
in order to improve the forecast
models
using
the
indexes
necessary to obtain the best
results.
Incorporating new diseases and
risk factor in the forecasting
models.
To improve the statistics of the
climatic, epidemic, ecological and
social variables that allows
diminishing
the
levels
of
uncertainty in the projections
adaptation measures ( Cont)
Immunization program
for the groups of high
risk and all population.
To maintain the current program of
vaccination and to priorities new programs
directed to the varicella (chicken pox) among
other important diseases.
Influenza vaccination program in ancient
applies using Influenza vaccines against the
agents circulating and before the peak of
Acute Respiratory Infections. Besides, to
continue the immunization program against
Haemophilus influenzae to achieve their
successful control; and to maintain
antimeningococcal immunization program.
In the future is necessary to carry out a
prevention program against Chicken pox
previous the forecasting increase.
Improvement of
sanitary conditions.
the
Increase of sanitary demands in all fields
(communal, drinking water , garbage,
sewage, foods and others)
Maintain contingency plans
Educational programs about environment care
with the participation of the community,
governments, and all sectors. Increase of
environment care projects. To improve
contingency plans.
Educational programs in
TV, in radio, news papers
and others.
Maintain the forecast of
the behavior of a group
of
communicable
diseases through IPK–
Epidemiological bulletin.
To expose results of the climate and health
researches that allow the best understanding
of the concepts, work methods and achieved
advances to settle down that contribute to a
risk perception to the variability climatic and
change and their impact on human health .
Distribution of the IPK – Bulletin at all of the
levels of the National Public Health System.
Implementation of new programs about
climate-health using all the way of
communication to population, governments
and others.
Exchange
information
with
scientific
and
researches working in
this task in the world.
To participate in international meetings,
congress, and others.
Looking for new projects with participation
with other countries.
To do the forecast for each province and
municipalities level.
Areas where the health sector can contribute to
protecting health under a changing climate
Decisions based within
the health sector
Health input into
decisions taken by
other sectors
Corvalan, 2006
• Prevention of climatesensitive diseases; e.g.
vaccines, bednets, water
and food safety.
• Disease surveillance
• Disease early warning
systems
• Disaster preparedness
• Scenario-based forecasts
of future risks
• Development of
intersectoral engagement
• Public education
• Planning decisions to
reduce impacts on climate
An overview of the kinds of decisions that can contribute to
protecting health under a changing climate
Climate & Health: Decision-Making Under Uncertainty
Health Risk Assessment
Research
Decision-making
domains
Surveillance
Local
Decisions
about:
All risks
(incl. health)
Climate-related
health risk as
policy criterion
Mitigation
(emissions
reduction)
International
Risk Management
Health risks
National
Health Sector
Health risk
reduction
(interventions:
addressing
combinations of
climate and nonclimate influences)
Immediate
Other Sectors
Health Sector
Long-term
Other Sectors
Corvalan, 2006
Used Climate
Prediction
Climate - Health Group.
SGP-037. Project-IAI
IMPORTANCE OF THE FORECASTING
AS ANTICIPATORY (OR PROACTIVE)
ADAPTATION MEASURE IN THE HUMAN
HEALTH SECTOR.
• Experiment and analysis tool.
• Tool for understanding.
• Early Warning System.
• Support tool for decision makers.
Bioclimatic Prediction System of Cuba - Early
Warning System.
BPSCEWS
Input and compile
information
Global
and
Regional
Scale
Data process
National Scale
CENCLIM
CPC and
CDC
NAO
MEI
QBO
First Steep
Update information.
Validation.
Formulation to the indexes.
Climatic patterns analyze.
Ortíz, et al., 2005
Decision maker and output
Action for preparation
Epidemiological bulletin for
Biometeorological forecast (monthly
frequencies) national and province scale.
Bioclimatic outlook quarterly months
Warning special emission
MT, TN, TOSC,
AP, VP, RH, DOA,
INS y RAD
Second Steep.
IPK: ARIs, ADDs, VM, BM, MD, VAR,
Climatic prediction models run
 Epidemiological prediction models
run.
NEU, VH
UNLAV: Focus AE, LD y BDH
Actions
Send warning systems and bulletin
health
for UNLAV and IPK witch
contribute of strategies in level different
of decision makers in health
Third Steep
Results, analyze and evaluation
Forecast preparation.
Risk maps edition.
To perfect the system of feedback and search new information
.
Diseases included in Early Warning System of Cuba.
Includes in
system
Not includes
Diseases
Acute diarrhoeal diseases
Viral hepatitis
Acute respiratory infections
Varicella (chicken pox)
Meningococcal diseases
Bacterial meningitis
Meningitis by Streptococcus
pneumoniae
Viral meningitis
Malaria
Dengue
Yellow fever
Leishmaniasis
Lectospira
Seasonal Climate Outlook. May – Agoust/2006.
Period of base line used 1961-1990 and current
condition 1991-2005.
0.391
0.582
0.773
0.964
1.155
1.345
1.536
1.727
1.918
2.109
above
Very Warm
Warm
Ortíz, et al., 2006. Available at monthly epidemiological bulletin of IPK
Seasonal Climate outlook (May – August/2006 )
according to IB t,1,C.
200 000
400 000
600 000
800 000
100 000 0
120 000 0
600 000
600 000
N
400 000
400 000
200 000
200 000
0
Prono_IB1.shp
0.935 - 1.016
1.016 - 1.159
1.159 - 1.272
1.272 - 1.467
200 000
0
400 000
600 000
800 000
100 000 0
120 000 0
Ortíz, et al., 2006. Available http://www.ipk.sld.cu/bolepid/2006e.htm
Climate outlook according to IB t,1,C. August/2006
-84
-82
-80
-78
-76
26
26
N
24
24
22
22
20
Esc. 1:250 000
20
Prono IB1
1.03 - 1.06
1.07 - 1.12
1.13 - 1.18
18
18
-84
-82
Ortíz, et al., 2006. Available
-80
-78
-76
http://www.ipk.sld.cu/bolepid/2006e.htm
Expected risk in some
diseases according to
Climate outlook for
Cuba.
Rate of per 100 000 habitants, expectation
attentions by Bacterial Meningitis. August/2006.
-84
-82
-80
-78
-76
26
26
N
24
24
22
22
Esc. 1:250 000
20
20
Prono MB
0
0.01 - 0.48
0.48 - 1.13
1.13 - 2.86
18
18
-84
-82
Ortíz, et al., 2006. Available
-80
-78
-76
http://www.ipk.sld.cu/bolepid/2006e.htm
Rate of per 100 000 habitants, expectation attentions by
Acute Respiratory Infections (ARIs). August/2006.
-84
-82
-80
-78
-76
N
24
24
22
22
Esc. 1:250 000
20
20
Prono IRA
1286.54
1605.03
2113.08
2483.38
-
1605.02
2113.07
2483.37
3245.73
18
18
-84
-82
Ortíz, et al., 2006. Available
-80
-78
-76
http://www.ipk.sld.cu/bolepid/2006e.htm
Forecasting number of focus Aedes aegypti
(hotspot). August/2006.
-84
-82
-80
-78
-76
N
24
24
22
22
Esc. 1:250 000
20
Pronostico
24 - 246
246 - 564
564 - 943
2560 - 2831
20
18
18
-84
-82
Ortíz, et al., 2006. Available
-80
-78
-76
http://www.ipk.sld.cu/bolepid/2006e.htm
Forecast and current values of ADDs. May 2005
-84
-82
-80
-78
26
-76
-84
-82
-80
-78
-76
N
24
24
22
22
22
22
24
24
N
Esc. 1:250 000
Esc: 1 000 000
20
20
LEYENDA
Prono_EDA
79.43 -132.9
133.0- 340.69
340.7- 480.88
480.89- 586.31
-84
-82
20
20
Prono EDA
73.19 - 132.9
133 - 340.69
340.7 - 480.88
480.89 - 683.04
18
18
26
-80
-78
-76
Ortíz, et al., 2005. Available
18
18
-84
-82
-80
-78
http://www.ipk.sld.cu/bolepid/2005e.htm
-76
Forecast and current values of ADDs. June /2005.
26
-84
-82
-80
-78
-84
-82
-80
-78
-76
N
24
24
22
22
22
22
24
24
N
Esc. 1:250 000
Esc: 1 000 000
20
20
LEYENDA
-84
20
20
Prono EDA
0 - 75.9
76 - 373.3
373.4 - 484.8
484.9 - 861.31
Prono_EDA
0 - 75.9
76- 373.3
373.4- 484.8
484.9 - 673.8
18
18
26
-76
-82
-80
-78
-76
Ortíz, et al., 2005. Available
18
18
-84
-82
-80
-78
-76
http://www.ipk.sld.cu/bolepid/2005e.htm
Forecast and current values of ARIs. July/2005.
-84
-8 2
-8 4
-8 0
-7 8
-82
-80
-78
-76
-7 6
N
N
24
24
22
22
24
24
22
22
LEYENDA
Esc. 1:250 000
Esc: 1 000 000
20
20
Prono_IRA
337.24
337.25 - 2128.39
2128.40- 2973.75
2973.76 - 3937.74
-8 2
Prono IRA
337.24
337.25 - 2128.39
2128.4 - 2973.75
2973.76 - 4002.80
18
18
18
18
-8 4
20
20
-8 0
-7 8
Ortíz, et al., 2005. Available
-84
-82
-80
-78
-7 6
http://www.ipk.sld.cu/bolepid/2005e.htm
-76
Forecast and current values of Varicella.
February /2006.
-84
-82
-80
-78
-76
26
26
-82
-80
-78
-76
26
26
-84
N
24
24
22
22
22
22
24
24
N
Esc. 1:250 000
20
20
LEYENDA
Esc: 1 000 000
Prono VAR
2.16
2.16 - 4.19
4.2 - 7.46
7.47 - 61.92
18
18
Prono_VAR
0
0.47 - 4.19
4.20 - 7.46
7.47 - 16.63
-84
20
20
-82
-80
-78
-76
Ortíz, et al., 2006. Available
18
18
-84
-82
-80
-78
-76
http://www.ipk.sld.cu/bolepid/2006e.htm
Forecast and current values of Varicella.
March /2006.
-84
-82
-80
-78
-76
-84
26
26
-82
-80
-78
-76
26
26
N
N
24
24
24
24
22
22
22
22
Esc. 1:250 000
20
Esc. 1:250 000
20
Prono VAR
1.62
1.63 - 18.81
18.82 - 31.82
31.83 - 55.41
Prono VAR
4.65
4.66 - 18.81
18.82 - 31.82
31.83 - 192.17
18
18
-84
-82
20
20
-80
-78
-76
Ortíz, et al., 2006. Available
18
18
-84
-82
-80
-78
-76
http://www.ipk.sld.cu/bolepid/2006e.htm
Conclusion

These section show that human health is an integrating theme of climate
variability and change. Population health is affected by climate and
particularly by climatic effects acting through natural disasters, climatesensitive diseases and through climate-sensitive sectors such as agriculture,
water, or human environmental.

In the Latin American and Caribbean region, increasing understanding of
the potential health impacts of climate variability and change, identifying as
those vulnerable to variability and long-term climate change (cyclones,
floods, and droughts) in Small Island.

Health is therefore both a key climate-sensitive sector in its own right, and
also provides an important justification for addressing climatic impacts on
other sectors .

The main roles for climate information in operational health decisions are:
1) Identification of climatically suitable or high-risk areas for particular
diseases
2) Early Warning Systems for climate-sensitive diseases can vary over time.
Conclusion. (cont’d)

These results demonstrate the studies of climate and health is necessary to
increase our knowledge of the effects of climate on human health; such
information is important for decision-makers for reducing the economicsocial impacts of climate variability and change in the region.

This study is innovative in the development of complex climate indices to
reflect climate anomalies at different scales, and to explain the mechanisms
and relationships between climatic conditions and diseases.

Based on our experience with the studies in Vulnerability and Adaptation
Assessment, it is clear that the climate prediction can be used to prepare
from climate variability and extreme events for the Climate Change,
including an estimation of costs.

Our experience also demonstrates that interdisciplinary collaboration and
the sharing of information, experience, and research methods among
sectors are critical for effective policy formulation and the development of
support tools for decision-makers.

The results of this study evidence a clear non lineal relationship between
the changes of the climatic variations and the changes of the patterns of
behavior of both diseases in a differentiated way
These documents is available in
the web site:


McMichael, A.J., D.H. Campbell-Lendrum, C.F. Corvalan,
K.L. Ebi, A. Githeko, J.D. Scheraga, and A. Woodward
(eds.). 2003. Climate Change and Human Health: Risks
and Responses. WHO, Geneva.
– Summary pdf available at
http://www.who.int/globalchange/publications/cchhsum
mary/
Kovats, R.D., K.L Ebi, and B. Menne. 2003. Methods of
Assessing Human Health Vulnerability and Public Health
Adaptation to Climate Change. WHO/Health Canada/UNEP.
– Pdf available at
http://www.who.dk/document/E81923.pdf





An Approach for Assessing Human Health Vulnerability and Public Health
Interventions to Adapt to Climate Change Kristie L. Ebi, R. Sari
Kovats, and Bettina Menne doi:10.1289/ehp.8430 (Pdf available at
http://dx.doi.org/) Online 11 July 2006.
Climate Variability and Change and their Potential Health Effects in
Small Island States: Information for Adaptation Planning in the
Health Sector Kristie L. Ebi, Nancy D. Lewis, and Carlos Corvalan
doi:10.1289/ehp.8429 (Pdf available at http://dx.doi.org/) Online 11
July 2006.
Assessment of Human Health Vulnerability to Climate Variability and
Change in Cuba Paulo Lázaro Ortíz Bultó, Antonio Pérez
Rodríguez, Alina Rivero Valencia, Nicolás León Vega, Manuel Díaz,
and Alina Pérez Carrera doi:10.1289/ehp.8434 (Pdf available at
http://dx.doi.org/) Online 11 July 2006.
Comparative Risk Assessment of the Burden of Disease from
Climate Change Diarmid Campbell-Lendrum and Rosalie Woodruff
doi:10.1289/ehp.8432 (Pdf available at http://dx.doi.org/) Online 11
July 2006.
Climate variability and change and their health effects in small
island states: information for adaptation planning in the health
sector. By K.L. Ebi, N.D. Lewis, C.F. Corvalán. Pdf available at
http://www.who.int/globalchange/climate/climatevariab/en/inde
x.html

Climate Change and Human Health book: Pdf available at
http://www.who.int/globalchange/climate/en/

Ecosystems and human well-being: a health synthesis, Pdf
available at http://www.who.int/globalchange/climate/en/

Using climate to predict infectious disease epidemics. Pdf
available at ttp://www.who.int/globalchange/climate/en/

Climate variability and change and their health effects in small
island states . Pdf available at
http://www.who.int/globalchange/climate/en/

Information package in environmental and occupational health.
Pdf available at http://www.who.int/globalchange/climate/en/

Climate and health. Pdf available at
http://www.who.int/globalchange/climate/en
Health Data Sources



World Health Report provides regional-level data
for all major diseases
– http://www.who.int/whr/en
– Annual data in Statistical Annex
WHO databases
– Malnutrition http://www.who.int/nutgrowth/db
– Water and sanitation
http://www.who.int/entity/water_sanitation_hea
lth/database/en
Ministry of Health
– Disease surveillance/reporting
branch
Health Data Sources –
Other
 UNICEF
at
http://www.unicef.org
 CRED-EMDAT provides data on
disasters
– http://www.em-dat.net
 Mission
hospitals
 Government district hospitals
Other Models
 MIASMA
– Global malaria model
 CiMSiM
and DENSim for dengue
– Weather and habitat-driven
entomological simulation model that
links with a simulation model of
human population dynamics to
project disease outbreaks
– http://daac.gsfc.nasa.gov/IDP/mode
ls/index.html
MARA/ARMA Model
Biological model that defines a set
of decision rules based on minimum
and mean temperature constraints
on
the
development
of
the
Plasmodium falciparum parasite and
the Anopheles vector, and on
precipitation constraints on the
survival and breeding capacity of the
mosquito
 CD-ROM $5 for developing countries
or can download components from
website: www.mara.org.za
