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.
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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 above Association between climate variability and the number of positive houses (hotspot) of the Aedes aegypti by climate variability according to indexes A rea High Ris k A rea Low Ris k 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 0.12 -0.2 -0.2 -0.2 0.1 Latitud .2 -0 .1 -0 0 -0. 0.1 .2 -0 .2 -0 -0.1 -0.1 22 0.03 -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