Training on Vulnerability and Adaptation Assessment for the Latin America and the Caribbean Region

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
• Emailpaulo.ortiz_at_insmet.cu or
  bulto01_at_yahoo.com

2
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 VA in the health
  sector by means of a case of study.

3
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 exposureresponse
  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
  exposureresponse relationship.

4
Health as an integrating issue in climate
variability and climate change
Corvalán, C., 2006
5
Climate variability influences human
Health, three way interconnected
6
Pathways from Driving Forces to Potential Health
Impacts
Corvalan et al., 2003
7
Steps in the Vulnerability and Adaptation
Assessment in health sector (Kovasts et, al 2003)

• Step 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.

8
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

9
Step 1 (contd)

• 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

10
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.

11
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

12
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.

13
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.

14
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.

15
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.

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Some Climate Trends Observed
17
Climate Change May Entail Changes in Variance, as
Well as Changes in Mean
18
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
Frequency distribution
Without climate change
with climate change
19
Trend Anomaly temperatures in the north and
south hemisphere (1860-1999)
North hemisphere
South hemisphere
20
Main Climate Trends Observed in Cuba During the
1990s
21
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.

22
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
• Migration
• Famine
• Sanitation
• Population

Ecological Change . Biodiversity Loss .
Communityre location . Nutrient cycle changes
Epidemiological Change Vector-Borne diseases
or not
Malaria
Yellow fever
Dengue
Meningococcal meningitis
Filariasis
ARIs
Others
ADDs
Hepatitis
23
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.

24
Simulation of impacts with the vectorial capacity
model
25
Parameters of the vectorial capacity

• V vectorial capacity is the daily rate at
  which
• future inoculations arise from an
  infective
• member of a non-immune community.
• Ma Composite index of the daily man-
• biting rate
• a Daily man biting habit is obtained from
• p Probability of the vector surviving through
  1 day
• n The parasite extrinsic incubation period in
  the vector

26
Expression to Malaria epidemic risk calculation
27
Expression to epidemic risk calculation from
models on climate and health used in Cuba
Ortíz et al., 2001
28
Some diseases of Climate Sensibility
29
High priority diseases identified in Brazil
Cities diseases Study Periods
Rio de Janeiro Dengue fever Jan, 1988 Dec, 2002
Rio de Janeiro Leptospirosis Jan, 1988 Dec, 2002
Rio de Janeiro Meningococcal Meningitis Jan, 1988 Dec, 2002
Recife Dengue fever Jan, 1995 Dec, 2002
Marabá Malaria Jan, 1992 Dec, 2002
30
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
31
Many different types of uncertainty relate to the
health effects of climate change
Source of uncertainty Examples
Problems with data Missing components or errors in data Noise in data associated with bias or incomplete observations Random sampling error and biases in a sample.
Problems with models (relationships between climate and health) 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.
Other sources of uncertainty 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
32
Case Study Cuba
33
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 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).
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.
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).
34
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 (Empirical Orthogonal
Functions)
CLIMATE INDEXES (IB1,IB2,..)
In Cuba
IB1 Describes the seasonal climate
patterns ? - 2
................ IB1 ........... ? 2 IB2
Describes the intraseasonal climate
patterns
They explain about 80 of the total climate
variance
Warm, dry, not rainy
Hot, humid, rainy
Transition seasons
Winter
Summer
(Ortíz et al., 1998, 2001)
35
Expression to anomalies in the different scales
of the variability calculation.
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
36
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.

37
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
38
Some diseases of Climate Sensibility
39
Association between climate variability and
viral hepatitis according to the indexes

Ortíz, et al., 2006
40
Association between climate variability and
acute diarrhoeal disease according to the indexes

Ortíz, et al., 2006
41
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
42
Association between climate variability and the
Meningitis a Neumococo according to the indices.
Ortíz, et al., 2006
43
Spatial - Temporal Distribution of some diseases
according to climate index for Cuba.
44
Behavior of the Varicella (chicken pox)
according to I-Moran
45
Behavior of the ADDs according to I-Moran
46
Behavior of the VH according to I-Moran
47
Distribution time - spatial of IBt,3,c
48

• Climate Change
• Scenarios.

49
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 exposure-response relationship
• Should include projections of how other relevant
  factors may change
• Uncertainty must be addressed explicitly

Kovats et al., 2003
50
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
51
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
52
Climatic change scenarios.
Ortíz, et al., 2006
53
Scenario of variability climate the Low
sensibility (Rates of change per decade) with
climate variability sensitivity the in the range
lt 0.70
Ortíz, et al., 2006
54
Scenario of variability climate the high
sensibility. (Rates of change per decade) with
climate variability sensitivity in the range gt
0.70
Ortíz, et al., 2006
55
Potential impact according to scenarios in Cuba.
Effects of high climate variability IBt,1,C Trend Diseases Effects Transmission way
Effects of high climate variability IBt,1,C _ Bronquial Asthma Decrease of the number of cases in winter Air-borne diseases
Effects of high climate variability IBt,1,C Air-borne diseases
Effects of high climate variability IBt,1,C Acute Respiratory infection A new epidemic peack on warm season Air-borne diseases
Effects of high climate variability IBt,1,C Air-borne diseases
Effects of high climate variability IBt,1,C Meningococcal diseases Increase of incidence in winter season Air-borne diseases
Effects of high climate variability IBt,1,C Air-borne diseases
Effects of high climate variability IBt,1,C Chicken pox Advance of the epidemic outbreak Air-borne diseases
Effects of high climate variability IBt,1,C Water-food borne diseases
Effects of high climate variability IBt,1,C Viral hepatitis Increase of the incidence in winter season Water-food borne diseases
Effects of high climate variability IBt,1,C Water-food borne diseases
Effects of high climate variability IBt,1,C Acute diarrhoeal diseases Advance the increase of incidence to winter months Water-food borne diseases
Effects of high climate variability IBt,1,C Vector Borne Diseases
Effects of high climate variability IBt,1,C Dengue fever More frequent epidemic outbreaks and change of seasonal patron and spatial Vector Borne Diseases
Ortíz et. al., 2006
56
Economic impact on Human Health due to
variability and climate change.
Climate - Health Group. PNCT Project-Cuba
57
Estimate health cost ( millions US) associated
with climate variability. Jan/2001-Mar/2002.
Ortíz et, al,. 2004
58
.
Economic Cost (million US) according to
scenarios 2010.
Ortíz el at. 2004
59

• Adaptation
• measures

Climate - Health Group. SGP-037. Project-IAI
60
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
61
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.
62
Areas where the health sector can contribute to
protecting health under a changing climate
Corvalan, 2006
63
An overview of the kinds of decisions that can
contribute to protecting health under a changing
climate
Corvalan, 2006
64

• Used Climate Prediction

Climate - Health Group. SGP-037. Project-IAI
65
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.

66
Bioclimatic Prediction System of Cuba - Early
Warning System.
Ortíz, et al., 2005
BPSC-EWS
Input and compile information
Data process
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

• First Steep
• Update information.
• Validation.
• Formulation to the indexes.
• Climatic patterns analyze.

Global and Regional Scale
National Scale
CENCLIM MT, TN, TOSC, AP, VP, RH, DOA, INS y
RAD
CPC and CDC NAO MEI QBO

• 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
IPK ARIs, ADDs, VM, BM, MD, VAR, NEU,
VH UNLAV Focus AE, LD y BDH

• Third Steep
• Results, analyze and evaluation
• Forecast preparation.
• Risk maps edition.

To perfect the system of feedback and search new
information
67
Diseases included in Early Warning System of Cuba.
68
Seasonal Climate Outlook. May Agoust/2006.
Period of base line used 1961-1990 and current
condition 1991-2005.
Ortíz, et al., 2006. Available at monthly
epidemiological bulletin of IPK
69
Seasonal Climate outlook (May August/2006 )
according to IB t,1,C.
Ortíz, et al., 2006. Available http//www.ipk.sld.
cu/bolepid/2006e.htm
70
Climate outlook according to IB t,1,C.
August/2006
Ortíz, et al., 2006. Available http//www.ipk.sld.
cu/bolepid/2006e.htm
71
Expected risk in some diseases according to
Climate outlook for Cuba.
72
Rate of per 100 000 habitants, expectation
attentions by Bacterial Meningitis. August/2006.
Ortíz, et al., 2006. Available http//www.ipk.sld.
cu/bolepid/2006e.htm
73
Rate of per 100 000 habitants, expectation
attentions by Acute Respiratory Infections
(ARIs). August/2006.
Ortíz, et al., 2006. Available http//www.ipk.sld.
cu/bolepid/2006e.htm
74
Forecasting number of focus Aedes aegypti
(hotspot). August/2006.
Ortíz, et al., 2006. Available http//www.ipk.sld.
cu/bolepid/2006e.htm
75
Forecast and current values of ADDs. May 2005
Ortíz, et al., 2005. Available http//www.ipk.sld.
cu/bolepid/2005e.htm
76
Forecast and current values of ADDs. June /2005.
Ortíz, et al., 2005. Available http//www.ipk.sld.
cu/bolepid/2005e.htm
77
Forecast and current values of ARIs. July/2005.
Ortíz, et al., 2005. Available http//www.ipk.sld.
cu/bolepid/2005e.htm
78
Forecast and current values of Varicella.
February /2006.
Ortíz, et al., 2006. Available http//www.ipk.sld.
cu/bolepid/2006e.htm
79
Forecast and current values of Varicella. March
/2006.
Ortíz, et al., 2006. Available http//www.ipk.sld.
cu/bolepid/2006e.htm
80
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, 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.

81
Conclusion. (contd)

• 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

82
These documents is available in the web site
83

• 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/global
  change/publications/cchhsummary/
• 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

84

• 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 doi10.1289/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 doi10.1289/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 doi10.1289/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
  doi10.1289/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/climateva
  riab/en/index.html

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• Climate Change and Human Health book Pdf
  available at http//www.who.int/globalchange/clima
  te/en/
• Ecosystems and human well-being a health
  synthesis, Pdf available at http//www.who.int/glo
  balchange/climate/en/
• Using climate to predict infectious disease
  epidemics. Pdf available at ttp//www.who.int/glob
  alchange/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

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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/wat
  er_sanitation_health/database/en
• Ministry of Health
• Disease surveillance/reporting branch

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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

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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/models/index.html

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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