* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Health_1_Formatted. ppt
Global warming hiatus wikipedia , lookup
Climate resilience wikipedia , lookup
Politics of global warming wikipedia , lookup
Climate engineering wikipedia , lookup
Global warming wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate change feedback wikipedia , lookup
Climate governance wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Solar radiation management wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Climate sensitivity wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change adaptation wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Economics of global warming wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Climate change in the United States wikipedia , lookup
Effects of global warming wikipedia , lookup
General circulation model wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Vulnerability and Adaptation Assessments Hands-On Training Workshop HUMAN HEALTH SECTOR 1A.1 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 Overview of the Potential Health Impacts of Climate Variability and Change 1A.3 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 Pathways for Weather to Affect Health: Example = Diarrheal Disease Distal Causes Temperature Humidity Precipitation Living conditions (water supply and sanitation) Food sources and hygiene practices Proximal Causes Infection Hazards Survival/ replication of pathogens in the environment Consumption of contaminated water Contamination of water sources Consumption of contaminated food Contamination of food sources Contact with infected persons Rate of person to person contact WHO Health Outcome Incidence of mortality and morbidity attributable to diarrhea Vulnerability (e.g. age and nutrition) Pathways from Driving Forces to Potential Health Impacts Corvalan et al., 2003 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 Climate Change May Entail Changes in Variance, as Well as Changes in Mean Temperature Extremes in the Caribbean, 1955-2000 Climate Variability and Change Impacts in the Caribbean DATE COUNTRY EVENT DEATH ESTIMATED COSTS (US$ million, 1998) 1974 Honduras Hurricane Fifi 7,000 1,331 1982/3 Bolivia, Ecuador, Peru El Niño 0 5,661 1997/98 Bolivia, Colombia, Ecuador, Peru El Niño 600 7,694 1998 Central America Hurricane Mitch 9,214 6,008 1998 Dominican Republic Hurricane Georges 235 2,193 Cuba Hurricane Georges 6 N/A Venezuela Landslide 25,000 N/A 1999 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. 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 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 Proportion of malaria case s and anomalie s in maximum te mpe rture : Ke nya 70 4 60 3 50 2 40 1 0 30 -1 20 Jan 97May SepJan 98May SepJan 99May Sep Temperature anomalies Percent of malaria cases in hospital 5 -2 Time Malaria cases A. Githeko, communication Dr. Githeko,personal personal communication Maximum temp Minimum Temp Climate Change and Malaria under Different Scenarios (2080) Increase: East Africa, Central Asia, Russian Federation Decrease: Central America, Amazon [within current vector limits] Change of consecutive months A1 > +2 +2 A2 -2 < -2 B1 B2 Van Lieshout et al. 2004 China Haze 10 January 2003 NASA Effect of Temperature Variation on Diarrheal Incidence in Lima, Peru Daily Diarrhea Admissions Daily Temperature Diarrhea increases by 8% for each 1ºC increase in temperature Checkley et al., 2000 Number of Cholera cases in Uganda 1997-2002 Number of cases 50000 40000 El Nino stops El Nino starts 30000 20000 10000 0 1996 1997 1998 1999 2000 Time in years 2001 2002 2003 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 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 Health Data to Determine the Current Burden of ClimateSensitive Diseases 1A.19 Questions to be Addressed What climate-sensitive diseases are important in the country or region? What factors other than climate should be considered? What is the current burden of these diseases? Water, sanitation, etc. Where are data available? Are health services able to satisfy current demands? Health Data Sources World Health Report provides regional-level data for all major diseases WHO databases http://www.who.int/whr/en Annual data in Statistical Annex Malnutrition http://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 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 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 WHO, 2005 72% of 1-year-olds immunized with 3 doses of DTP 5.8% of gross domestic product spent on health Seychelles National Communication Methods and Tools for V&A Assessment in the Health Sector 1A.25 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 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? Methods of Assessing Human Health Vulnerability and Public Health Adaptation to Climate Change Kovats et al., 2003 1A.28 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 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 Case Study: Risk of VectorBorne 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 Sources of Uncertainty Data Models Missing data or errors in data 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 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) Comparative Risk Assessment Greenhouse gas emissions scenarios Time 2020s 2050s Global climate modelling: 2080s 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 2020s 2050s 2080s 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 Exposure: Alternative Future Projections of GHG Emissions Unmitigated current GHG emissions trends Stabilization at 750 ppm CO2-equivalent Stabilization at 550 ppm CO2-equivalent 1961-1990 levels of GHGs with associated climate Campbell-Lendrum et al., 2003 Source: UK Hadley Centre models 8 Relative Risk of Deaths and Injuries in Inland Floods in 2030, by Region 7 s550 s750 UE 5 4 3 2 1 Wpr B Wpr A Sear D Sear B Eur C Eur B Eur A Emr D Emr B Amr D Amr B Amr A Afr E 0 Afr D Relative Risk 6 Relative Risk of Diarrheoa in 2030, by Region 1.1 Climate s550 scenarios, ass750 function of GHG UE emissions 1.08 1.04 1.02 1 0.98 0.96 Wpr B Wpr A Sear D Sear B Eur C Eur B Eur A Emr D Emr B Amr D Amr B Amr A Afr E 0.94 Afr D Relative Risk 1.06 Estimated Death and DALYs Attributable to Climate Change 2000 Floods 2020 Malaria Diarrhea Malnutrition 120 100 80 60 40 20 Deaths (thousands) Campbell-Lendrum et al., 2003 0 2 4 6 8 DALYs (millions) 10 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 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 http://www.who.int/peh/burden/burdenindex.h tml] Climate change document will be published soon The website [http://www.mara.org.za] contains prevalence and population data, and regional and country-level maps 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 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. 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 Mean Temperature (°C) 40 38 36 34 32 30 28 26 24 22 20 18 .1 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 16 Proportion of M osquitoes Surviving One Day Relationship between Temperature and Daily Survivorship of Anopheles Relationship between Temperature and Time Required for Parasite Development 120 100 Days 80 60 40 20 0 17 19 21 23 25 27 29 31 Mean Temperature (°C ) 33 35 37 39 Mean Temperature (°C) 39 37 35 33 31 29 27 25 23 21 19 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 17 Proportion Surviving Proportion of Vectors Surviving Time Required for Parasite Development Mozambique – Endemic Malaria Season Length Mozambique – Endemic Malaria Prevalence Mozambique – Endemic Malaria Prevalence by Age 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 1A.58 Malaria in Zimbabwe Cases by Month Source: South African Malaria Research Programme Ebi et al., In press 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 Methods Baseline climatology determined COSMIC was used to generate Zimbabwespecific 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 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 Ebi et al., In press Projected mean monthly temperature and precipitation (1990-2100) 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 Ebi et al., In press Increase in maximum temperatures +0.6°C Decrease in minimum temperatures -0.2 °C 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 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 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 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 Assignment of Fuzzy Logic Values to Climate Variables Fuzzy Logic Value for Mean Temperature 1.2 Fuzzy Value 1 0.8 0.6 0.4 0.2 39.5 37.5 35.5 33.5 31.5 29.5 27.5 25.5 23.5 21.5 19.5 17.5 0 Mean Temperature (°C) Fuzzy Logic Value for Minimum Temperature 1.2 1.2 1 1 Precipitation (mm) Minimum Temperature (°C) 6.5 6.3 6.1 5.9 5.7 5.5 5.3 5.1 4.9 4.7 4.5 4.3 84 80 76 72 68 64 60 56 52 48 44 40 36 32 28 24 20 16 0 8 0 12 0.2 4 0.2 4.1 0.4 3.9 0.4 0.6 3.7 0.6 0.8 3.5 Fuzzy Value 0.8 0 Fuzzy Value Fuzzy Logic Value for Precipitation 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 UKMO S750 ECD stabilization scenario with 4.5°C climate sensitivity Model output Precipitation Temperature Ebi et al., In press Rainy season (ONDJFMA) increase in precipitation of 8.5% from 1990 to 2100 Annual mean temperature increase by 3.5°C from 1990 to 2100, with October temperatures increasing more than July temperatures. Baseline Ebi et al., In press 2025 Ebi et al., In press 2050 Ebi et al., In press 2075 Ebi et al., In press 2100 Ebi et al., In press 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 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 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 19611990 using the IPCC IS92A scenario, simulation results of HADCM2, GFDL, and BMRC, and MAGICC/SCENGEN Sudan – Projected Increase in Transmission Potential of Malaria in 2030 Sudan – Projected Increase in Transmission Potential of Malaria in 2060 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 Methods for Determining a Health Adaptation Baseline 1A.81 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? 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? Kovats et al., 2003 Identify strengths and weaknesses, as well as threats and opportunities to implementation 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 Options for Adaptations to Reduce the Health Impacts of Climate Change Health Outcome Legislative Technical Educational-advisory Cultural & Behavioral Thermal stress Building guidelines Housing, public buildings, urban planning, air conditioning Early warning systems Clothing, siesta Extreme weather events Planning laws, economic incentives for building Urban planning, storm shelters Early warning systems Use of storm shelters Vector control, vaccination, impregnated bednets, sustainable surveillance, prevention & control programmes Health education Water storage practices Screening for pathogens, improved water treatment & sanitation Boil water alerts Washing hands and other behavior, use of pit latrines Vector-borne diseases Water-borne diseases McMichael et al. 2001 Watershed protection laws, water quality regulation Screening the Theoretical Range of Response Options – Malaria Theoretical Range of Choice Technically feasible? Effective? Environmentally acceptable? Financially Feasible? Socially and Legally Acceptable? Closed/Open (Practical Range of Choice) Improved public health infrastructure Yes Low Yes Sometimes Yes Open Forecasting & early warning systems Yes Medium Yes Often Yes Open Public information & education Yes Low Yes Yes Yes Open Control of vector breeding sites Yes Yes Spraying - no Yes Sometimes Open Impregnated bed nets Yes Yes Yes Yes Yes Open Prophylaxis Yes Yes Yes Only for the few Yes Closed for many Vaccination No Ebi and Burton, submitted Closed Analysis of the Practical Range of Response Options – Malaria Theoretical Range of Choice Technically viable? Financial Human skills & capability? institutional capacity? Compatible with current policies? Target of opportunity? Improved public health infrastructure Yes Low Low Yes Yes Forecasting & early warning systems Yes Yes Yes Yes Yes Public information & education Yes Yes Sometimes Yes Yes Control of vector breeding sites Yes Sometimes Sometimes Yes Yes Impregnated bed nets Yes Sometimes Yes Yes Yes Prophylaxis Yes Sometimes Yes Yes Yes Ebi and Burton, submitted