1. Methods and Tools for the Human Health Sector Kristie L. Ebi, Ph.D., MPH Washington, DC USA Kebi@exponent.com V&A Assessment Hands-On Training Workshop April 2005

2. Outline 1.Overview of the potential health impacts of climate variability & change 2.Health data to determine the current burden of climate-sensitive diseases 3.Methods and tools for V&A assessment in the health sector 4.Methods for determining a health adaptation baseline

3. Overview of the Potential Health Impacts of Climate Variability & 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 & 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 Philip Wijmans, LWF/ACT Mozambique, March 2000 Immediate deaths and injuries Non specific increases in mortality Infectious diseases – leptospirosis, hepatitis, diarrhoeal, respiratory, & vector- borne diseases Exposure to toxic substances Mental health effects Increased demands on health systems

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

17. El Nino starts El Nino stops Dr. Githeko, personal communication

18. Resources McMichael AJ, Campbell-Lendrum DH, Corvalan CF, Ebi KL, Githeko A, Scheraga JD, Woodward A (eds.). Climate Change and Human Health: Risks and Responses. WHO, Geneva, 2003. –Summary pdf available at http://www.who.int/globalchange/publications/cchhsumma ry/ Kovats RD, Ebi KL, Menne B. Methods of Assessing Human Health Vulnerability and Public Health Adaptation to Climate Change. WHO/Health Canada/UNEP, 2003. –Pdf available at http://www.who.dk/document/E81923.pdf

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

20. Questions to be Addressed What climate-sensitive diseases are important in your 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 –http://www.who.int/whr/enwww.who.int/whr/en –Annual data in Statistical Annex WHO databases –Malnutrition http://www.who.int/nutgrowth/dbhttp://www.who.int/nutgrowth/db –Water and sanitation http://www.who.int/entity/water_sanitation_health/datab ase/en Ministry of Health –Disease surveillance/reporting branch

22. Health Data Sources - Other UNICEF at http://www.unicef.orghttp://www.unicef.org CRED-EMDAT provides data on disasters –http://www.em-dat.nethttp://www.em-dat.net 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 = 158/1000 –72% of 1-year-olds immunized with 3 doses of DTP 5.8% of gross domestic product spent on health World Health Report 2005

24. WHO Region Afr-E (Countries with High Child & Very High Adult Mortality) World Health Report 2004 Population360,965,000 Total deaths6,007,000 HIV/AIDS1,616,000 Diarrheal diseases356,000 Malaria579,000 Protein-energy malnutrition 54,000

25. Seychelles National Communication

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

27. 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

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

29. Methods of Assessing Human Health Vulnerability and Public Health Adaptation to Climate Change Kovats et al. 2003

30. 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

31. 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

32. Case Study: Risk of Vector- Borne Diseases in Portugal 4 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

33. 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

34. Estimating the Global Health Impacts of Climate Change Campbell-Lendrum et al. 2003 (pdf available) 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)?

35. 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

36. 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) –VBD – dengue and Falciparum malaria –Inland and coastal floods (mortality) –Heat and cold related CVD mortality Campbell-Lendrum et al. 2003

37. 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

39. Climate scenarios, as function of GHG emissions

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

41. 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 non-climatic factors Campbell-Lendrum et al. 2003

42. Environmental Burden of Disease Introduction and Methods: Assessing the Environmental Burden of Disease at National and Local Levels by A Pruss-Ustun, C Mathers, C Corvalan, and A Woodward [pdf available at http://www.who.int/peh/burden/burdenindex.html] Climate change document will be published soon

43. The website [http://www.mara.org.za] contains prevalence and population data, and regional and county-level maps

45. 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

46. 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.

47. 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 or can download components from website

48. Relationship Between Temperature and Daily Survivorship of Anopheles

49. Proportion of Vectors Surviving Time Required for Parasite Development

50. Relationship Between Temperature and Time Required for Parasite Development

53. Mozambique – Endemic Malaria Season Length

56. Mozambique – Endemic Malaria Prevalence

57. Mozambique – Endemic Malaria Prevalence by Age

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

60. 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 1989-1996 Recent high-altitude outbreaks Cases by Month Source: South African Malaria Research Programme Ebi et al. Climatic Change

61. 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. Climatic Change

62. 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. Climatic Change

63. 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. Climatic Change

64. 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. Climatic Change

65. Scenarios Climate sensitivity –High = 4.5ºC –Low = 1.4ºC Equivalent carbon dioxide (ECD) analogues to the 350 ppmv and 750 ppmv greenhouse gas emission stabilization scenarios of the IPCC SAR Ebi et al. Climatic Change

66. 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. Climatic Change

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

68. Assignment of Fuzzy Logic Values to Climate Variables

69. 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. Climatic Change

70. 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. Climatic Change

71. Baseline Ebi et al. Climatic Change

72. 2025 Ebi et al. Climatic Change

73. 2050 Ebi et al. Climatic Change

74. 2075 Ebi et al. Climatic Change

75. 2100 Ebi et al. Climatic Change

76. 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. Climatic Change

77. 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/inde x.html

78. 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

79. Sudan – Projected Increase in Transmission Potential of Malaria in 2030

80. Sudan – Projected Increase in Transmission Potential of Malaria in 2060

81. 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

82. Methods for Determining a Health Adaptation Baseline

83. Questions for Designing Adaptation Policies & 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?

84. 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

85. 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

86. Policy Analysis of Flooding Adaptation Strategies, Policies and Measures in the UK Burton and Ebi, in preparation

88. Thank You