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1A.1 Vulnerability and Adaptation Assessments Hands-On Training Workshop HUMAN HEALTH SECTOR.

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Presentation on theme: "1A.1 Vulnerability and Adaptation Assessments Hands-On Training Workshop HUMAN HEALTH SECTOR."— Presentation transcript:

1 1A.1 Vulnerability and Adaptation Assessments Hands-On Training Workshop HUMAN HEALTH SECTOR

2 Outline Overview of the potential health impacts of climate variability and change Health data to determine the current burden of climate-sensitive diseases Methods and tools for V&A assessment in the health sector Methods for determining a health adaptation baseline

3 1A.3 Overview of the Potential Health Impacts of Climate Variability and Change

4 Topics Pathways for weather to affect health Potential health impacts of climate change Extreme weather events Temperature Floods Vector-borne diseases Diseases related to air pollution Diarrheal diseases

5 Pathways for Weather to Affect Health: Example = Diarrheal Disease Temperature Humidity Precipitation Distal Causes Proximal CausesInfection HazardsHealth Outcome Living conditions (water supply and sanitation) Food sources and hygiene practices Survival/ replication of pathogens in the environment Contamination of water sources Rate of person to person contact Consumption of contaminated water Consumption of contaminated food Contact with infected persons Incidence of mortality and morbidity attributable to diarrhea Vulnerability (e.g. age and nutrition) Contamination of food sources WHO

6 Corvalan et al., 2003 Pathways from Driving Forces to Potential Health Impacts

7 Drivers of Health Issues Population density Urbanization Public health infrastructure Economic and technologic development Environmental conditions Populations at risk Poor Children Increasing population of elderly residents Immunocompromised

8 Climate Change May Entail Changes in Variance, as Well as Changes in Mean

9 Temperature Extremes in the Caribbean, 1955-2000

10 Climate Variability and Change Impacts in the Caribbean DATE COUNTRYEVENTDEATHESTIMATED COSTS (US$ million, 1998) 1974HondurasHurricane Fifi7,0001,331 1982/3Bolivia, Ecuador, PeruEl Niño05,661 1997/98Bolivia, Colombia, Ecuador, Peru El Niño6007,694 1998Central AmericaHurricane Mitch9,2146,008 1998Dominican RepublicHurricane Georges2352,193 CubaHurricane Georges6N/A 1999VenezuelaLandslide25,000N/A Fuente: ECLAC, América Latina y El Caribe: El Impacto de los Desastres Naturales en el Desarrollo, 1972-1999, LC/MEX/L.402; OFDA, Venezuela- Floods, Fact Sheet #10, 1/12/ 2000.

11 2000 Flood in Mozambique Heavy rains from Cyclones Connie and Eline in February 2000 caused large-scale flooding of the Limpopo, Incomati, Save, and Umbeluzi rivers Environmental degradation and poor river system management and protection contributed to the crisis 700 people died, 250,000 people were displaced, and 950,000 required humanitarian assistance (of which 190,000 were children under the age of 5) 14,800 people were rescued by helicopter

12 Health Impacts of Floods Immediate deaths and injuries Nonspecific increases in mortality Infectious diseases – leptospirosis, hepatitis, diarrheal, respiratory, and vector-borne diseases Exposure to toxic substances Mental health effects Increased demands on health systems Philip Wijmans, LWF/ACT Mozambique, March 2000

13 Dr. Githeko, personal communication A. Githeko, personal communication

14 Climate Change and Malaria under Different Scenarios (2080) Increase: East Africa, Central Asia, Russian Federation Decrease: Central America, Amazon [within current vector limits] A1 B2 A2 B1 Van Lieshout et al. 2004

15 China Haze 10 January 2003 NASA

16 Daily Temperature Daily Diarrhea Admissions Diarrhea increases by 8% for each 1ºC increase in temperature Effect of Temperature Variation on Diarrheal Incidence in Lima, Peru Checkley et al., 2000

17 El Nino starts El Nino stops

18 Resources 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 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

19 1A.19 Health Data to Determine the Current Burden of Climate- Sensitive Diseases

20 Questions to be Addressed What climate-sensitive diseases are important in the country or region? What is the current burden of these diseases? What factors other than climate should be considered? Water, sanitation, etc. Where are data available? Are health services able to satisfy current demands?

21 Health Data Sources World Health Report provides regional-level data for all major diseases Annual data in Statistical Annex WHO databases Malnutrition Water and sanitation ase/en Ministry of Health Disease surveillance/reporting branch

22 Health Data Sources – Other UNICEF at CRED-EMDAT provides data on disasters Mission hospitals Government district hospitals

23 Mozambique Total population = 18,863,000 Annual population growth rate = 2.4% Life expectancy at birth = 45 years Under age 5 mortality rate = 158/1,000 72% of 1-year-olds immunized with 3 doses of DTP 5.8% of gross domestic product spent on health WHO, 2005

24 Seychelles National Communication

25 1A.25 Methods and Tools for V&A Assessment in the Health Sector

26 Methods and Tools Qualitative assessments Methods of assessing human health vulnerability to climate change MARA/ARMA – climate suitability for stable malaria transmission WHO Global Burden of Disease Comparative Risk Assessment Environmental Burden of Disease Other models

27 Qualitative Assessments Available data allow for qualitative assessment of vulnerability For example, given current burden of diarrheal diseases and projected changes in precipitation, will vulnerability remain the same, increase, or decrease?

28 1A.28 Methods of Assessing Human Health Vulnerability and Public Health Adaptation to Climate Change Kovats et al., 2003

29 Methods for: Estimating the current distribution and burden of climate-sensitive diseases Estimating future health impacts attributable to climate change Identifying current and future adaptation options to reduce the burden of disease Kovats et al., 2003

30 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

31 Case Study: Risk of Vector- Borne Diseases in Portugal Four qualitative scenarios developed of changes in climate and in vector populations Vector not present Focal distribution of vector Widespread distribution of vector Change from focal to potentially regional distribution Expert judgment determined likely risk under each scenario for 5 vector-borne diseases Kovats et al., 2003

32 Sources of Uncertainty Data Missing data or errors in data Models Uncertainty regarding predictability of the system Uncertainty introduced by simplifying relationships Other Inappropriate spatial or temporal data Inappropriate assumptions Uncertainty about predictive ability of scenarios Kovats et al., 2003

33 Estimating the Global Health Impacts of Climate Change What will be the total potential health impact caused by climate change (2000 to 2030)? How much of this could be avoided by reducing the risk factor (i.e. stabilizing greenhouse gas (GHG) emissions)? Campbell-Lendrum et al., 2003 (pdf available)

34 Comparative Risk Assessment 2020s 2050s 2080s Greenhouse gas emissions scenarios Global climate modelling: Generates series of maps of predicted future climate Health impact model: Estimates the change in relative risk of specific diseases Campbell-Lendrum et al., 2003 Time 2080s2050s2020s

35 Criteria for Selection of Health Outcomes Sensitive to climate variation Important global health burden Quantitative model available at the global scale Malnutrition (prevalence) Diarrhoeal disease (incidence) Vector-borne diseases – dengue and falciparum malaria Inland and coastal floods (mortality) Heat and cold related CVD mortality Campbell-Lendrum et al., 2003

36 Exposure: Alternative Future Projections of GHG Emissions Unmitigated current GHG emissions trends Stabilization at 750 ppm CO 2 -equivalent Stabilization at 550 ppm CO 2 -equivalent 1961-1990 levels of GHGs with associated climate Source: UK Hadley Centre models Campbell-Lendrum et al., 2003


38 Climate scenarios, as function of GHG emissions

39 Floods Malaria Diarrhea Malnutrition 020406080100120246810 DALYs (millions)Deaths (thousands) 2000 2020 Estimated Death and DALYs Attributable to Climate Change Campbell-Lendrum et al., 2003

40 Conclusions Climate change may already be causing a significant burden in developing countries Unmitigated climate change is likely to cause significant public health impacts out to 2030 Largest impacts from diarrhea, malnutrition, and vector-borne diseases Uncertainties include: Uncertainties in projections Effectiveness of interventions Changes in nonclimatic factors Campbell-Lendrum et al., 2003

41 Environmental Burden of Disease A. Prüss-Üstün, C. Mathers, C. Corvalan, and A. Woodward. 2003. Introduction and Methods: Assessing the Environmental Burden of Disease at National and Local Levels [pdf available at html] Climate change document will be published soon

42 The website [] contains prevalence and population data, and regional and country-level maps


44 Climate and Stable Malaria Transmission Climate suitability is a primary determinant of whether the conditions in a particular location are suitable for stable malaria transmission A change in temperature may lengthen or shorten the season in which mosquitoes or parasites can survive Changes in precipitation or temperature may result in conditions during the season of transmission that are conducive to increased or decreased parasite and vector populations

45 Climate and Stable Malaria Transmission (continued) Changes in precipitation or temperature may cause previously inhospitable altitudes or ecosystems to become conducive to transmission. Higher altitudes that were formerly too cold or desert fringes that were previously too dry for mosquito populations to develop may be rendered hospitable by small changes in temperature or precipitation.

46 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:

47 Relationship between Temperature and Daily Survivorship of Anopheles

48 Relationship between Temperature and Time Required for Parasite Development

49 Proportion of Vectors Surviving Time Required for Parasite Development



52 Mozambique – Endemic Malaria Season Length



55 Mozambique – Endemic Malaria Prevalence

56 Mozambique – Endemic Malaria Prevalence by Age


58 1A.58 Climate Suitability for Stable Malaria Transmission in Zimbabwe Under Different Climate Change Scenarios Ebi et al., In press Objective: to look at the range of responses in the climatic suitability for stable falciparum malaria transmission under different climate change scenarios in Zimbabwe

59 Malaria in Zimbabwe Patterns of stable transmission follow pattern of precipitation and elevation (which in turn influences temperature) > 9,500 deaths and 6.4 million cases between 1989 and 1996 Recent high-altitude outbreaks Cases by Month Source: South African Malaria Research Programme Ebi et al., In press

60 Methods Baseline climatology determined COSMIC was used to generate Zimbabwe- specific scenarios of climate change; changes were added to baseline climatology Outputs from COSMIC were used as inputs for the MARA/ARMA (Mapping Malaria Risk in Africa) model of climate suitability for stable Plasmodium falciparum malaria transmission Ebi et al., In press

61 Data Inputs Climate data Mean 60 year climatology of Zimbabwe on a 0.05° lat/long grid (1920-1980) Monthly minimum and maximum temperature and total precipitation COSMIC output Projected mean monthly temperature and precipitation (1990-2100) Ebi et al., In press

62 Climate in Zimbabwe Rainy warm austral summer October-April Dry and cold May-September Heterogeneous elevation-dictated temperature range Strong interannual and decadal variability in precipitation Decrease in precipitation in the last 100 years (about 1% per decade) Temperature changes 1933-1993 Increase in maximum temperatures +0.6°C Decrease in minimum temperatures -0.2 °C Ebi et al., In press

63 GCMs Canadian Centre for Climate Research (CCC) United Kingdom Meteorological Office (UKMO) Goddard Institute for Space Studies (GISS) Henderson-Sellers model using the CCM1 at NCAR (HEND) Ebi et al., In press

64 Scenarios Climate sensitivity High = 4.5°C Low = 1.4°C Equivalent carbon dioxide (ECD) analogues to the 350 ppmv and 750 ppmv GHG emission stabilization scenarios of the IPCC SAR Ebi et al., In press

65 Assumptions No change in the monthly range in minimum and maximum temperatures Permanent water bodies do not meet the precipitation requirements Climate did not change between the baseline (1920-1980) and 1990 Ebi et al., In press

66 Fuzzy Logic Value Fuzzy logic boundaries established for minimum, mean temperature, and precipitation 0 = unsuitable 1 = suitable for seasonal endemic malaria Ebi et al., In press

67 Assignment of Fuzzy Logic Values to Climate Variables

68 Climate Suitability Criteria Fuzzy values assigned to each grid For each month, determined the lowest fuzzy value for precipitation and mean temperature Determined moving 5-month minimum fuzzy values Compared these with the fuzzy value for the lowest monthly average of daily minimum temperature Assigned the lowest fuzzy value Ebi et al., In press

69 UKMO S750 ECD stabilization scenario with 4.5°C climate sensitivity Model output Precipitation Rainy season (ONDJFMA) increase in precipitation of 8.5% from 1990 to 2100 Temperature Annual mean temperature increase by 3.5°C from 1990 to 2100, with October temperatures increasing more than July temperatures. Ebi et al., In press

70 Baseline Ebi et al., In press

71 2025 Ebi et al., In press

72 2050 Ebi et al., In press

73 2075 Ebi et al., In press

74 2100 Ebi et al., In press

75 Conclusions Assuming no future human-imposed constraints on malaria transmission, changes in temperature and precipitation could alter the geographic distribution of stable malaria transmission in Zimbabwe Among all scenarios, the highlands become more suitable for transmission The lowveld and areas currently limited by precipitation show varying degrees of change The results illustrate the importance of using several climate scenarios Ebi et al., In press

76 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

77 Sudan National Communication Using an Excel spreadsheet, modeled malaria based on relationships described in MIASMA Calculated monthly changes in transmission potential for the Kordofan Region for the years 2030-2060, relative to the period 1961- 1990 using the IPCC IS92A scenario, simulation results of HADCM2, GFDL, and BMRC, and MAGICC/SCENGEN

78 Sudan – Projected Increase in Transmission Potential of Malaria in 2030

79 Sudan – Projected Increase in Transmission Potential of Malaria in 2060

80 Sudan – Malaria Projections Malaria in Kordofan Region could increase significantly during the winter months in the absence of effective adaptation measures The transmission potential during these months is 75% higher than without climate change Under HADCM2, the transmission potential in 2060 is more than double baseline Transmission potential is projected to decrease during May-August due to increased temperature

81 1A.81 Methods for Determining a Health Adaptation Baseline

82 Questions for Designing Adaptation Policies and Measures Adaptation to what? Is additional intervention needed? What are the future projections for the outcome? Who is vulnerable? On scale relevant for adaptation Who adapts? How does adaptation occur? When should interventions be implemented? How good or likely is the adaptation?

83 Current and Future Adaptation Options What is being done now to reduce the burden of disease? How effective are these policies and measures? What measures should begin to be implemented to increase the range of possible future interventions? When and where should new policies be implemented? Identify strengths and weaknesses, as well as threats and opportunities to implementation Kovats et al., 2003

84 Public Health Adaptation to Climate Change Existing risks Modifying existing prevention strategies Reinstitute effective prevention programs that have been neglected or abandoned Apply win/win or no-regrets strategies New risks

85 Options for Adaptations to Reduce the Health Impacts of Climate Change Health OutcomeLegislativeTechnicalEducational-advisoryCultural & Behavioral Thermal stressBuilding guidelinesHousing, public buildings, urban planning, air conditioning Early warning systemsClothing, siesta Extreme weather events Planning laws, economic incentives for building Urban planning, storm shelters Early warning systemsUse of storm shelters Vector-borne diseasesVector control, vaccination, impregnated bednets, sustainable surveillance, prevention & control programmes Health educationWater storage practices Water-borne diseasesWatershed protection laws, water quality regulation Screening for pathogens, improved water treatment & sanitation Boil water alertsWashing hands and other behavior, use of pit latrines McMichael et al. 2001

86 Screening the Theoretical Range of Response Options – Malaria Ebi and Burton, submitted

87 Analysis of the Practical Range of Response Options – Malaria Ebi and Burton, submitted

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