training on vulnerability and adaptation assessment for the latin america and the caribbean region...
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Training on Training on Vulnerability and Vulnerability and Adaptation Assessment for the Latin Adaptation Assessment for the Latin
America and the Caribbean RegionAmerica and the Caribbean Region
HUMAN HEALTH SECTORHUMAN HEALTH SECTOR
Paulo Lázaro Ortíz Bultó, PhDPaulo Lázaro Ortíz Bultó, PhD Climate Center-Meteorological Institute. CubaClimate Center-Meteorological Institute. Cuba
Email:Email:[email protected]@insmet.cu or [email protected] or [email protected]
Goals of trainingGoals of training An approach and methods needs to increase our An approach and methods needs to increase our
understanding of the issue of climate variability, climate understanding of the issue of climate variability, climate change and health assessment.change and health assessment.
A general discussion on the potential impacts of climate A general discussion on the potential impacts of climate variability and change on health sector in the region.variability and change on health sector in the region.
A general discussion about of steps in a vulnerability and A general discussion about of steps in a vulnerability and adaptation assessment. adaptation assessment.
Provides concepts and examples of coping and adaptive Provides concepts and examples of coping and adaptive capacity in the region.capacity in the region.
A general discussion about the data, tools and methods A general discussion about the data, tools and methods available to assess V&A in the health sector available to assess V&A in the health sector by means of by means of a case of studya 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.
Climate variability and change
Human Health
Water Resources
Agriculture & Food Security
Energy & Built Environment
Disasters
Health as an integrating issue in climate Health as an integrating issue in climate
variability and climate changevariability and climate change
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
Corvalan et al., 2003
Pathways from Driving Forces to Pathways from Driving Forces to Potential Health ImpactsPotential Health Impacts
Steps in the Vulnerability and Adaptation Steps in the Vulnerability and Adaptation Assessment in health sector Assessment in health sector (Kovasts et, al 2003)(Kovasts et, al 2003)
StepStep 1. Determine the scope of the assessment.
Step 2. Describe the current distribution and burden of climate-sensitive diseases.
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.
StepStep 1: Include to Identify Indicators in 1: Include to Identify Indicators in Sectors and Examine Current Conditions.Sectors and Examine Current Conditions.
Key sectorsKey sectors– Solicit or survey local decision-makers and stakeholdersSolicit or survey local decision-makers and stakeholders
– Is appropriate rank or set priorities according to climate Is appropriate rank or set priorities according to climate sensitivity and importancesensitivity and importance
– Define baseline conditions using current data related to Define baseline conditions using current data related to sectors and indicatorssectors and indicators
StepStep 1: (cont’d) 1: (cont’d)
Some Indicators of impactsSome Indicators of impacts Increased disease incidenceIncreased disease incidence Increased disease prevalenceIncreased disease prevalence New records of diseaseNew records of disease Severe forms of diseasesSevere forms of diseases Increased case fatality rateIncreased case fatality rate Cases exceed medical capacityCases exceed medical capacity
DemographyDemography population, age structure, migration indexpopulation, age structure, migration index
Step Step 2: Include to description the current 2: Include to description the current burden and recent trend in the incidence burden and recent trend in the incidence and prevalence of climate-sensitive health and prevalence of climate-sensitive health determinant and develop Baseline determinant and develop Baseline Scenarios (without climate change)Scenarios (without climate change)
Examine recent trends and seasonal variation and Examine recent trends and seasonal variation and
the relationship climate variables, includingthe relationship climate variables, including::
Identification the signal climate in the patterns diseases.Identification the signal climate in the patterns diseases.
To analyze association with exposure to weather or climate To analyze association with exposure to weather or climate variability.variability.
Step 3Step 3: Include the key aspects to : Include the key aspects to address for specific health outcomeaddress for specific health outcome
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
The specifics questions include the following:
Step 4Step 4: Include the results of other : Include the results of other assessments should be includes to assessments should be includes to better understand.better understand.
SSectors ectors such as:such as:
Agriculture and food supply, water resources, disasters on coastal Agriculture and food supply, water resources, disasters on coastal and river flooding. and river flooding.
Review the feedback from changes in population health status in Review the feedback from changes in population health status in these sectors.these sectors.
Step 5Step 5: Requires the generation and : Requires the generation and using climate scenarios. Climate using climate scenarios. Climate scenarios are now available for a range scenarios are now available for a range
of time scalesof time scales..
Examine differentExamine different::
Models of climate change should include projections as other Models of climate change should include projections as other relevant factors may change in the future, such as population relevant factors may change in the future, such as population growth, and other relevant factors. growth, and other relevant factors.
The potential future impact of climate variability and change on The potential future impact of climate variability and change on health may be estimated using a variety of methods. health may be estimated using a variety of methods.
Step 6Step 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 7Step 7: I: Identify possible adaptation measures that could be undertaken over the short and long term.
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.
Goals of this step
Some Climate Trends Some Climate Trends ObservedObserved
Climate Change May Entail Changes in Variance, as Well as Changes in Mean
with climate change
Trend
Climate change and ENSO event frequency distribution. Sea surface temperature Anomalies (SSTA) inin the region Niño 3 about scenarios without and with climate change)
Without climate change
Frequency distribution
Trend Anomaly Trend Anomaly temperatures in the north and south in the north and south hemisphere hemisphere (1860-1999)(1860-1999)
North hemispherehemisphere
South hemisphere
Main Climate Trends Observed in Cuba During the 1990sMain 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 SectorResearch in multiples scale and data in Health Sector
Research:Research: Is need to conduct community based Is need to conduct community based assessments and systematic research on the issues of assessments and systematic research on the issues of climate change impacts in our countries and in all regionclimate change impacts in our countries and in all region..
Multiples ScaleMultiples Scale: Local, regional and national scales are : Local, regional and national scales are interconnected in supporting and facilitating action on interconnected in supporting and facilitating action on climate change, is need for data at multiple scales and climate change, is need for data at multiple scales and research that links scales to understand these relationships. research that links scales to understand these relationships.
The DataThe Data: Innovative approaches to health and climate : Innovative approaches to health and climate assessment are needed and should consider the role of assessment are needed and should consider the role of socio-cultural diversity present among countries. This socio-cultural diversity present among countries. This requires both qualitative and quantitative data, and the requires both qualitative and quantitative data, and the collection of long term data sets on standard health collection of long term data sets on standard health outcomes at comparable temporal and spatial scales. They outcomes at comparable temporal and spatial scales. They favor the development appropriate applications for the favor the development appropriate applications for the sector health. sector health.
How are the relationships between variability and climate change and epidemiological pattern changes?
Epidemiological Change Vector-Borne diseases
or not
Variability and Climate Change
Ecological Change . Biodiversity Loss
. Communityre location
. Nutrient cycle changes
Filariasis
Malaria
Dengue
Yellow fever
Meningococcal meningitis
Others
Changes in the biological transmition. Dynamics of the vector
.Dynamics of the pathogens
Socio-EconomicChange•Migration•Famine
•Sanitation•Population
ARIsADDs Hepatitis
MethodsMethods
Research methods used so far include Research methods used so far include predictive modelling, analogue methods and predictive modelling, analogue methods and early effects. Predictive models include early effects. Predictive models include biological models (e,g malaria), empirical biological models (e,g malaria), empirical statistical models (e.g, temperature-mortality statistical models (e.g, temperature-mortality relationships), the used the complex index relationships), the used the complex index simulation variability climate change and other simulation variability climate change and other processes (e.g, relationship climate index and processes (e.g, relationship climate index and diseases) and integrated assessment (IA) diseases) and integrated assessment (IA) models. Is need the balance empirical analysis models. Is need the balance empirical analysis with scenario-based methods and to integrate with scenario-based methods and to integrate the different methods through, for example, IA the different methods through, for example, IA methods. The outcome of an assessment may methods. The outcome of an assessment may not necessarily be quantitative for to be useful not necessarily be quantitative for to be useful to stakeholders.to stakeholders.
Simulation of impacts with the Simulation of impacts with the vectorial capacity modelvectorial capacity model
Parameters of the vectorial Parameters of the vectorial capacitycapacity
VV: v: vectorial capacity is the daily rate at whichectorial capacity is the daily rate at which future inoculations arise from an infectivefuture inoculations arise from an infective member of a non-immune community.member of a non-immune community.MaMa: C: Composite index of the daily man- omposite index of the daily man- biting ratebiting rateaa : D : Daily man biting habit is obtained aily man biting habit is obtained fromfrom
pp: P: Probability of the vector surviving through 1 day robability of the vector surviving through 1 day
nn : T : The parasite extrinsic incubation period in the he parasite extrinsic incubation period in the vector vector
Expression to Malaria epidemic risk Expression to Malaria epidemic risk calculationcalculation
100xmRmT
iRiTER
Expression to epidemic risk calculation from Expression to epidemic risk calculation from
models models on climate and health used in Cubaon climate and health used in Cuba
k
iia
cI1
01
1
k
ii
m
accI
1
10
1
k
iia
cI1
12
1
Ortíz et al., 2001
Some diseases of Climate Sensibility
High priority diseases identified in Brazil
Cities Cities diseasesdiseases Study Periods Study Periods
Rio de JaneiroRio de Janeiro
Dengue feverDengue fever Jan, 1988 – Dec, 2002Jan, 1988 – Dec, 2002
LeptospirosisLeptospirosis JanJan, 1988 – , 1988 – DecDec, 2002, 2002
Meningococcal Meningococcal
MeningitisMeningitis
Jan, Jan, 1988 – 1988 – DecDec, 2002, 2002
RecifeRecife Dengue feverDengue fever Jan,Jan, 1995 – 1995 –DecDec, 2002, 2002
MarabáMarabá MalariaMalaria Jan,Jan, 1992 – 1992 – DecDec, 2002, 2002
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 Examples
Problems with data1. Missing components or errors in data2. “Noise” in data associated with bias or incomplete
observations3. Random sampling error and biases in a sample.
Problems with models (relationships between climate and health)
1. Known processes but unknown functional relationships or errors in structure of model
2. Known structure but unknown or erroneous values of some important parameters.
3. 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.
4. Uncertainty introduced by approximating or simplifying relationships within the model.
Other sources of uncertainty
1. Ambiguously defined concepts or terms2. Inappropriate spatial or temporal units (such as in data on
exposure to climate or weather)3. Inappropriateness of or lack of confidence in the
underlying assumptions4. 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
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.
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.
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).
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.
Ecological data:
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).
To define climate characteristics and its health effects in To define climate characteristics and its health effects in
Cuba, a complex approach has been developedCuba, a complex approach has been developed
Determinate
by
EOF
(Empirical Orthogonal Functions)
IncludeMaximum and Minimum Temperatures•Daily Oscillation Temperatures•Relative Humidity•Vapor pressure•Atmospheric pressure•Rainfall•ENSO influence (MEI)
CLIMATE INDEXES(IB1,IB2,..)
(Ortíz et al., 1998, 2001)
IB1 Describes the seasonal climate patterns
- 2 ................ IB1 ........... + 2
IB2 Describes the intraseasonal climate patterns
Warm, dry, not rainy
Hot, humid, rainyTransition
seasonsWinter Summer
In Cuba:
They explain about 80% of the total climate variance
Expression to anomalies in the different scales of Expression to anomalies in the different scales of
the variability calculation.the variability calculation.
n
prttIB 1,,,
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.Interpretation of the indices. IBIBt,1,ct,1,c describes inter-monthly and inter-seasonal describes inter-monthly and inter-seasonal
variation; Includes maximum and minimum mean variation; Includes maximum and minimum mean temperature, precipitation, atmospheric pressure, temperature, precipitation, atmospheric pressure, vapor pressure, and relative humidity. vapor pressure, and relative humidity.
IBIBt,2,ct,2,c describes seasonal and inter-annual variation; describes seasonal and inter-annual variation; Includes solar radiation and sunshine duration as Includes solar radiation and sunshine duration as factors that affect temperature and humidity. factors that affect temperature and humidity. Positive values are associated with a high solar Positive values are associated with a high solar energy level.energy level.
IBIBt,3,ct,3,c describes inter-annual and decadal scale describes inter-annual and decadal scale variation and includes the same climate variables variation and includes the same climate variables as IBas IBt,1,ct,1,c
IBIBt,4,ct,4,c describes the relationships among describes the relationships among socioeconomic variables and can be interpreted as socioeconomic variables and can be interpreted as life quality, or the degree of poverty as their life quality, or the degree of poverty as their influence disease riskinfluence disease risk..
Year
Ran
ge
0
2
4
6
8
10
12
14
16
18
20
22
0
2
4
6
8
10
12
14
16
18
20
22 1
981
1982
198
3
1984
198
5
198
6
1987
1988
198
9
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Low risk
Moderate risk
Moderate risk
High risk
High risk
Behavior of the ranges by months to determine the level risk climate of the variation according to the IB t,3C.
Ortíz, et al., 2006
Some diseases of Climate Sensibility
Association between climate variability and viral hepatitis according to the indexes
149 237 325 413 502 590 678 766 855 949 above
Area Low Risk
Area High Risk
Ortíz, et al., 2006
Association between climate variability and acute diarrhoeal disease according to the indexes
5126 10252 15378 20503 25629 30755 35881 41007 46133 51258 above
Area Low Risk
Area High Risk
Ortíz, et al., 2006
341 682 1024 1365 1706 2048 2389 2730 3072 3413 above
Area Low Risk
Area High Risk
Association between climate variability and the number of positive houses (hotspot) of the Aedes aegypti by
climate variability according to indexes
Ortíz, et al., 2006
Association between climate variability and the Meningitis a Neumococo according to the indices.
0.045 0.591 1.136 1.682 2.227 2.773 3.318 3.864 4.409 4.955 above
Area High Risk
Area Low Risk
Ortíz, et al., 2006
Spatial - Temporal Distribution of some diseases according to
climate index for Cuba.
Behavior of the Behavior of the Varicella (chicken pox)Varicella (chicken pox) according to I-Moranaccording to I-Moran
-82 -80 -78 -76
Longitud
21
22
23
La
titud
-0.1
0.2
0.4
-0.79
-0.30
0.19
0.67
1.16
1.65
Behavior of the ADDs according to I-MoranBehavior of the ADDs according to I-Moran
-82 -80 -78 -76
Longitud
21
22
23
Latit
ud
-0.3
-0.3
-0.2
-0.
2
-0.2
-0.
2
-0.2
-0.
2
-0.1
-0.1
-0.1
-0.1
-0.0
0.0
0.1
0.1
0.1
-0.33
-0.24
-0.15
-0.06
0.03
0.12
Behavior of the VH according toBehavior of the VH according to I-Moran I-Moran
-82 -80 -78 -76
Longitud
21
22
23
Latit
ud
-0.
8
-0.6
-0.4
-0.
2
-0.
2
-0.
1
-0.
1
0.1
0.1
0.3
0.3
0.5
-1.31
-0.96
-0.60
-0.25
0.11
0.46
Distribution time - spatial of Distribution time - spatial of IB IBt,3,ct,3,c
-1.309 -1.018 -0.727 -0.436 -0.145 0.145 0.436 0.727 1.018 1.309 above
LONG
LAT
Jan
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Feb
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Mar
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Apr
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
May
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Jun
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Jul
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Aug
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Sep
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Oct
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Nov
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Dec
19.5
20.5
21.5
22.5
23.5
-86 -82 -78 -74 -70
Climate ChangeClimate Change
Scenarios.Scenarios.
Estimate Potential Future Estimate Potential Future Health ImpactsHealth Impacts
Requires using climate scenariosRequires using climate scenarios Can use top-down or bottom-up Can use top-down or bottom-up
approachesapproaches– Models can be complex spatial models Models can be complex spatial models
or be based on a simple exposure-or be based on a simple exposure-response relationshipresponse relationship
Should include projections of how other Should include projections of how other relevant factors may changerelevant factors may change
Uncertainty must be addressed explicitlyUncertainty must be addressed explicitly
Kovats et al., 2003
Estimate Potential Future Estimate Potential Future Health ImpactsHealth Impacts
In our case are usedIn our case are used::
Scenarios of Climate change (and other Scenarios of Climate change (and other changes) are used as inputs into a model on changes) are used as inputs into a model on climate and health.climate and health.
Models spatial combination with models Models spatial combination with models Generalised Autoregressive Conditional Generalised Autoregressive Conditional Heteroskedasticity (GARCH) with dummy Heteroskedasticity (GARCH) with dummy variable for the model on climate and health.variable for the model on climate and health.
Ortíz et al., 2004, 2006
MACVAH/AREEC MACVAH/AREEC ModelModel Model Model MACVAH/AREECMACVAH/AREEC (Model of the Anomaly (Model of the Anomaly
Variability and Climate Change Impact on Human Variability and Climate Change Impact on Human Health- Assessment Risk Epidemic and Costs Health- Assessment Risk Epidemic and Costs Estimate).Estimate).
This Model describes the Anomaly Climate This Model describes the Anomaly Climate variability and Change for the impact on the variability and Change for the impact on the Human Health used as input the scenarios output Human Health used as input the scenarios output of climate change and health models proposes of climate change and health models proposes for diseases, generating maps of risk epidemic for diseases, generating maps of risk epidemic for Cuba using GIS. Finally, were estimated the for Cuba using GIS. Finally, were estimated the impact of Costs to variability and change. The impact of Costs to variability and change. The spatial correlation explains for each disease the spatial correlation explains for each disease the capacity to dissemination of the epidemic and capacity to dissemination of the epidemic and the range of the correlation describes the trend the range of the correlation describes the trend epidemic.epidemic.
Ortíz,2004
Climatic change scenarios.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
100 0 100 200 Kilometers
N
-84
-84
-80
-80
-76
-76
20 20
24 24
IB1,t,c - L0.42 - 0.490.50 - 0.590.60 - 0.69
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
Ortíz, et al., 2006
100 0 100 200 Kilometers
IB1,t,c - H1.01 - 1.051.06- 1.111.12 - 1.38
N
-84
-84
-80
-80
-76
-76
20 20
24 24
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
Scenario of variability climate the high sensibility. (Rates of change per decade) with climate variability
sensitivity in the range > 0.70
Ortíz, et al., 2006
Potential impact according to scenarios in Cuba.Potential impact according to scenarios in Cuba.
Effects of high climate variability IBt,1,C
Trend Diseases Effects Transmission way
_ Bronquial Asthma Decrease of the number of cases in winter
Air-borne diseases
++ Acute Respiratory infection A new epidemic peack on warm season
+ Meningococcal diseases Increase of incidence in winter season
++ Chicken pox Advance of the epidemic outbreak
Water-food borne diseases++ Viral hepatitis Increase of the incidence
in winter season
++ Acute diarrhoeal diseases Advance the increase of incidence to winter
months
Vector Borne Diseases
++ Dengue fever More frequent epidemic outbreaks and change of
seasonal patron and spatial
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$) associated with climate variability. Jan/2001-Mar/2002.
Diseases
Cost of
Attention
Cost of hospitalization
Restricted activity day
Treatment cost
Cost of Service
of Urgency
Total Cost
HV 8 874.06 8 657.10 917 50.0 5 505.0 1 236.79 116 022.95 ADDs 373 073.6 175 067.95 547 059.2 76 064.6 36 463.4 1 207 728.75
Dengue Fever
- - - 3 745 605.66 3 745 605.66
Meningitis by
Neumoco*
- 231 318.00 - - 231 318.00
Total Cost
5 300 675.36
* All cases of admission in hospitals.
Ortíz et, al,. 2004
..
Diseases IC Cost ofIC
AD Cost AD Total Cost
ARIs 329 976 43 2021.44 98 993 33 775 21.87 77 477 442.87
ADDs 136 423 18 067 862 40 927 7 994 680.18 26 06542.18
VH 10 860 1 438 298 3 258 1 937 109.06 3 375 407.06
V 19 200 25 42 848 - - 2 542 848.00
MD 3 196 - 3 196 2 556 800.00 2 556 800.00
MD * 11 523 - 11 523 9 218 400.00 9 218 400.00
* With epidemic General Cost 121 233 440.11 IC: Increase of cases AD: Cases of admission in hospitals
Ortíz el at. 2004
Economic Cost (million US$) according to scenarios 2010.
Climate - Health Group.
SGP-037. Project-IAI
Adaptation measures
Some examples of adaptation measures to climate Some examples of adaptation measures to climate variability and change in Cuba. variability and change in Cuba. (Ortíz, el al 2006)(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) 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 the sanitary conditions.
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. To do the forecast for each province and municipalities level.
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.
Decisions based within the health sector
Health input into decisions taken by
other sectors
• Prevention of climate-sensitive 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
Areas where the health sector can contribute to Areas where the health sector can contribute to
protecting health under a changing climateprotecting health under a changing climate
Corvalan, 2006
Climate & Health: Decision-Making Under Uncertainty
Decisions about:
Risk Management
Health Risk Assessment
Health Sector
Health risk reduction (interventions: addressing combinations of climate and non-climate influences)
Other Sectors
Long-term
Immediate
Mitigation (emissions reduction)
Climate-related health risk as policy criterion
Local
National
International
Health Sector
Other Sectors
Surveillance
Research
Health risks
All risks (incl. health)
Decision-making domains
An overview of the kinds of decisions that can contribute to An overview of the kinds of decisions that can contribute to
protecting health under a changing climateprotecting health under a changing climate
Corvalan, 2006
Used Climate Used Climate PredictionPrediction
Climate - Health Group.
SGP-037. Project-IAI
IMPORTANCE OF THE FORECASTING IMPORTANCE OF THE FORECASTING AS ANTICIPATORY (OR PROACTIVE) AS ANTICIPATORY (OR PROACTIVE)
ADAPTATION MEASURE IN THE HUMAN ADAPTATION MEASURE IN THE HUMAN HEALTH SECTOR.HEALTH SECTOR.
IMPORTANCE OF THE FORECASTING IMPORTANCE OF THE FORECASTING AS ANTICIPATORY (OR PROACTIVE) AS ANTICIPATORY (OR PROACTIVE)
ADAPTATION MEASURE IN THE HUMAN ADAPTATION MEASURE IN THE HUMAN HEALTH SECTOR.HEALTH SECTOR.
• Experiment and analysis tool.
• Tool for understanding.
• Early Warning System.
• Support tool for decision makers.
Action for preparationEpidemiological bulletin for Biometeorological forecast (monthly frequencies) national and province scale.Bioclimatic outlook quarterly monthsWarning special emission
First SteepUpdate information.Validation.Formulation to the indexes.Climatic patterns analyze.
Second Steep.Climatic prediction models run Epidemiological prediction models run.
Actions Send warning systems and bulletin health for UNLAV and IPK witch contribute of strategies in level different of decision makers in health
Input and compile information Data process Decision maker and output
National ScaleGlobal and Regional Scale
CPC and CDC
NAOMEIQBO
CENCLIM
MT, TN, TOSC, AP, VP, RH, DOA, INS y RAD
IPK: ARIs, ADDs, VM, BM, MD, VAR, NEU, VH
UNLAV: Focus AE, LD y BDH
To perfect the system of feedback and search new information
Third SteepResults, analyze and evaluationForecast preparation.Risk maps edition.
BPSC-EWS
Bioclimatic Prediction System of Cuba - Early Warning System.
Ortíz, et al., 2005
.
Diseases Acute diarrhoeal diseases
Viral hepatitis Acute respiratory infections
Varicella (chicken pox) Meningococcal diseases
Bacterial meningitis Meningitis by Streptococcus
pneumoniae Viral meningitis
Malaria
Includes in system
Dengue Yellow fever Leishmaniasis
Not includes
Lectospira
Diseases included in Early Warning System of Cuba.
Seasonal Climate Outlook. May – Agoust/2006. Seasonal Climate Outlook. May – Agoust/2006. Period of base line used 1961-1990 and current Period of base line used 1961-1990 and current
condition 1991-2005.condition 1991-2005.
Ortíz, et al., 2006. Available at monthly epidemiological bulletin of IPK
Very Warm 0.391 0.582 0.773 0.964 1.155 1.345 1.536 1.727 1.918 2.109 above
Warm
Seasonal Climate outlook (May – August/2006 ) Seasonal Climate outlook (May – August/2006 ) according to according to IB IB t,1,Ct,1,C..
Prono_IB1.shp0.935 - 1.0161.016 - 1.1591.159 - 1.2721.272 - 1.467
N
200000
200000
400000
400000
600000
600000
800000
800000
1000000
1000000
1200000
1200000
0 0
200000 200000
400000 400000
600000 600000
Ortíz, et al., 2006. Available http://www.ipk.sld.cu/bolepid/2006e.htm
Climate outlook according to Climate outlook according to IB IB t,1,C. t,1,C. August/2006August/2006
Esc. 1:250 000
N
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
Prono IB11.03 - 1.061.07 - 1.121.13 - 1.18
Ortíz, et al., 2006. Available 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 Rate of per 100 000 habitants, expectation attentions by Bacterial Meningitis. August/2006.attentions by Bacterial Meningitis. August/2006.
Esc. 1:250 000
N
Prono MB00.01 - 0.480.48 - 1.131.13 - 2.86
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
Ortíz, et al., 2006. Available http://www.ipk.sld.cu/bolepid/2006e.htm
Rate of per 100 000 habitants, expectation attentions by Rate of per 100 000 habitants, expectation attentions by
AAcute Respiratory Infections (ARIs). August/2006(ARIs). August/2006..
Esc. 1:250 000
N
Prono IRA1286.54 - 1605.021605.03 - 2113.072113.08 - 2483.372483.38 - 3245.73
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
Ortíz, et al., 2006. Available http://www.ipk.sld.cu/bolepid/2006e.htm
Forecasting number of focus Aedes aegypti (hotspot). August/2006August/2006..
N
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
Pronostico24 - 246246 - 564564 - 9432560 - 2831
Esc. 1:250 000
Ortíz, et al., 2006. Available http://www.ipk.sld.cu/bolepid/2006e.htm
Forecast and current values of ADDs. May 2005Forecast and current values of ADDs. May 2005
N
Prono_EDA79.43 -132.9133.0- 340.69340.7- 480.88480.89- 586.31
LEYENDA
Esc: 1 000 000
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18
18
20
20
22
22
24
24
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
N
Prono EDA73.19 - 132.9133 - 340.69340.7 - 480.88480.89 - 683.04
Esc. 1:250 000
Ortíz, et al., 2005. Available http://www.ipk.sld.cu/bolepid/2005e.htm
Forecast and current values of ADDs. June /2005.Forecast and current values of ADDs. June /2005.
N
Prono_EDA0 - 75.976- 373.3373.4- 484.8484.9 - 673.8
LEYENDA
Esc: 1 000 000
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18
18
20
20
22
22
24
24
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
N
Prono EDA0 - 75.976 - 373.3373.4 - 484.8484.9 - 861.31
Esc. 1:250 000
Ortíz, et al., 2005. Available http://www.ipk.sld.cu/bolepid/2005e.htm
Forecast and current values of Forecast and current values of ARIs. July/2005.ARIs. July/2005.
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
Prono_IRA337.24337.25 - 2128.392128.40- 2973.752973.76 - 3937.74
LEYENDA
Esc: 1 000 000
N
Esc. 1:250 000
N
Prono IRA337.24337.25 - 2128.392128.4 - 2973.752973.76 - 4002.80
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
Ortíz, et al., 2005. Available http://www.ipk.sld.cu/bolepid/2005e.htm
Forecast and current values of Varicella. Forecast and current values of Varicella. February /2006.February /2006.
N
LEYENDA
Esc: 1 000 000
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18
18
20
20
22
22
24
24
26
26
Prono_VAR00.47 - 4.194.20 - 7.467.47 - 16.63
Esc. 1:250 000
N
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
Prono VAR2.162.16 - 4.194.2 - 7.467.47 - 61.92
Ortíz, et al., 2006. Available http://www.ipk.sld.cu/bolepid/2006e.htm
Forecast and current values of VaricellaForecast and current values of Varicella.. March /2006. March /2006.
Esc. 1:250 000
N
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
Prono VAR1.621.63 - 18.8118.82 - 31.8231.83 - 55.41
Esc. 1:250 000
N
-84
-84
-82
-82
-80
-80
-78
-78
-76
-76
18 18
20 20
22 22
24 24
26 26
Prono VAR4.654.66 - 18.8118.82 - 31.8231.83 - 192.17
Ortíz, et al., 2006. Available http://www.ipk.sld.cu/bolepid/2006e.htm
ConclusionConclusion 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, climate-sensitive 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.Conclusion. (cont’d)(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 economic-social 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 These documents is available in the web site:the web site:
McMichael, A.J., D.H. Campbell-Lendrum, C.F. Corvalan, K.L. McMichael, A.J., D.H. Campbell-Lendrum, C.F. Corvalan, K.L. Ebi, A. Githeko, J.D. Scheraga, and A. Woodward (eds.). Ebi, A. Githeko, J.D. Scheraga, and A. Woodward (eds.). 2003. Climate Change and Human Health: Risks and 2003. Climate Change and Human Health: Risks and Responses. WHO, Geneva.Responses. WHO, Geneva.– Summary pdf available at Summary pdf available at
http://www.who.int/globalchange/publications/cchhsumhttp://www.who.int/globalchange/publications/cchhsummary/mary/
Kovats, R.D., K.L Ebi, and B. Menne. 2003. Methods of Kovats, R.D., K.L Ebi, and B. Menne. 2003. Methods of Assessing Human Health Vulnerability and Public Health Assessing Human Health Vulnerability and Public Health Adaptation to Climate Change. WHO/Health Canada/UNEP.Adaptation to Climate Change. WHO/Health Canada/UNEP.– Pdf available at Pdf available at
http://www.who.dk/document/E81923.pdfhttp://www.who.dk/document/E81923.pdf
An Approach for Assessing Human Health Vulnerability and Public Health An Approach for Assessing Human Health Vulnerability and Public Health Interventions to Adapt to Climate Change Kristie L. Ebi, R. Sari Interventions to Adapt to Climate Change Kristie L. Ebi, R. Sari Kovats, and Bettina Menne doi:10.1289/ehp.8430 (Kovats, and Bettina Menne doi:10.1289/ehp.8430 (PdfPdf available at available at http://http://dx.doi.orgdx.doi.org//) Online 11 July 2006.) Online 11 July 2006.
Climate Variability and Change and their Potential Health Effects in Climate Variability and Change and their Potential Health Effects in Small Island States: Information for Adaptation Planning in the Small Island States: Information for Adaptation Planning in the Health Sector Kristie L. Ebi, Nancy D. Lewis, and Carlos Corvalan Health Sector Kristie L. Ebi, Nancy D. Lewis, and Carlos Corvalan doi:10.1289/ehp.8429 (doi:10.1289/ehp.8429 (PdfPdf available at available at http://http://dx.doi.orgdx.doi.org//) Online 11 ) Online 11 July 2006.July 2006.
Assessment of Human Health Vulnerability to Climate Variability Assessment of Human Health Vulnerability to Climate Variability and Change in Cuba Paulo Lázaro Ortíz Bultó, Antonio Pérez and Change in Cuba Paulo Lázaro Ortíz Bultó, Antonio Pérez Rodríguez, Alina Rivero Valencia, Nicolás León Vega, Manuel Díaz, Rodríguez, Alina Rivero Valencia, Nicolás León Vega, Manuel Díaz, and Alina Pérez Carrera doi:10.1289/ehp.8434 (and Alina Pérez Carrera doi:10.1289/ehp.8434 (PdfPdf available at available at http://http://dx.doi.orgdx.doi.org//) Online 11 July 2006.) Online 11 July 2006.
Comparative Risk Assessment of the Burden of Disease from Comparative Risk Assessment of the Burden of Disease from Climate Change Diarmid Campbell-Lendrum and Rosalie Woodruff Climate Change Diarmid Campbell-Lendrum and Rosalie Woodruff doi:10.1289/ehp.8432 (doi:10.1289/ehp.8432 (PdfPdf available at available at http://http://dx.doi.orgdx.doi.org//) Online 11 ) Online 11 July 2006.July 2006.
Climate variability and change and their health effects in small Climate variability and change and their health effects in small island states: information for adaptation planning in the health island states: information for adaptation planning in the health sector. By K.L. Ebi, N.D. Lewis, C.F. Corvalán. sector. By K.L. Ebi, N.D. Lewis, C.F. Corvalán. PdfPdf available at available at http://www.who.int/globalchange/climate/climatevariab/en/index.htmlhttp://www.who.int/globalchange/climate/climatevariab/en/index.html
Climate Change and Human Health book: Climate Change and Human Health book: PdfPdf available at available at http://www.who.int/globalchange/climate/en/http://www.who.int/globalchange/climate/en/
Ecosystems and human well-being: a health synthesis, Ecosystems and human well-being: a health synthesis, PdfPdf available at available at http://www.who.int/globalchange/climate/en/http://www.who.int/globalchange/climate/en/
Using climate to predict infectious disease epidemics. Using climate to predict infectious disease epidemics. PdfPdf available at available at ttp://www.who.int/globalchange/climate/enttp://www.who.int/globalchange/climate/en//
Climate variability and change and their health effects in small Climate variability and change and their health effects in small island states . island states . PdfPdf available at available at http://www.who.int/globalchange/climate/en/http://www.who.int/globalchange/climate/en/
IInformation package in environmental and occupational health. nformation package in environmental and occupational health. PdfPdf available at available at http://www.who.int/globalchange/climate/en/http://www.who.int/globalchange/climate/en/
Climate and health. Climate and health. PdfPdf available at available at
http://www.who.int/globalchange/climate/enhttp://www.who.int/globalchange/climate/en
Health Data SourcesHealth Data Sources
World Health Report provides regional-level data World Health Report provides regional-level data for all major diseasesfor all major diseases– http://www.who.int/whr/enhttp://www.who.int/whr/en– Annual data in Statistical AnnexAnnual data in Statistical Annex
WHO databasesWHO databases– Malnutrition Malnutrition http://www.who.int/nutgrowth/dbhttp://www.who.int/nutgrowth/db– Water and sanitation Water and sanitation
http://www.who.int/entity/water_sanitation_healthttp://www.who.int/entity/water_sanitation_health/database/enh/database/en
Ministry of HealthMinistry of Health– Disease surveillance/reporting Disease surveillance/reporting
branchbranch
Health Data Sources – Health Data Sources – OtherOther
UNICEF at UNICEF at http://www.unicef.org http://www.unicef.org CRED-EMDAT provides data on CRED-EMDAT provides data on
disastersdisasters– http://www.em-dat.nethttp://www.em-dat.net
Mission hospitalsMission hospitals Government district hospitalsGovernment district hospitals
Other ModelsOther Models
MIASMAMIASMA– Global malaria modelGlobal malaria model
CiMSiM and DENSim for dengueCiMSiM and DENSim for dengue– Weather and habitat-driven Weather and habitat-driven
entomological simulation model that entomological simulation model that links with a simulation model of links with a simulation model of human population dynamics to project human population dynamics to project disease outbreaksdisease outbreaks
– http://daac.gsfc.nasa.gov/IDP/models/http://daac.gsfc.nasa.gov/IDP/models/index.htmlindex.html
MARA/ARMA ModelMARA/ARMA Model Biological model that defines aBiological model that defines a set of set of
decision rules based on minimum and decision rules based on minimum and mean temperature constraints on the mean temperature constraints on the development of the Plasmodium development of the Plasmodium falciparum parasite and the Anopheles falciparum parasite and the Anopheles vector, and on precipitation constraints vector, and on precipitation constraints on the survival and breeding capacity of on the survival and breeding capacity of the mosquitothe mosquito
CD-ROM $5 for developing countries or CD-ROM $5 for developing countries or can download components from website: can download components from website: www.mara.org.zawww.mara.org.za