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 Email:paulo.ortiz@insmet.cu or bulto01@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 V&A 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 exposure–response relationship) the characteristics of the population, such as its demographic structure. • The exposurethe climate-related hazard, including the character, magnitude, and rate of climate variation. • The adaptationmeasuresandactionsin place to reduce the burden of a specific adverse health outcome (the adaptation baseline), the effectiveness of which may influence the exposure–response relationship.

4. Health as an integrating issue in climate variability and climate change Corvalán, C., 2006

5. Distribution and quality of water Life cycle of disease vectors and host/vector relationships Ecosystem dynamics of predator/prey relationships 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) • Step1. 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: (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

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 • 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:

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. • 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

16. 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 temperatureAnomalies (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 temperaturesin the north and south hemisphere (1860-1999) Northhemisphere 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

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 diseaseand 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 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 timet, 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,cdescribes 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,cdescribes 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 diseaseaccording 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