Methods and Tools for the Human Health Sector

Methods and Tools for the Human Health Sector

Methods and Tools for the Human Health Sector Kristie L. Ebi, Ph.D., MPH Washington, DC USA [email protected] V&A Assessment Hands-On Training Workshop April 2005 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

Overview of the Potential Health Impacts of Climate Variability & Change 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 & 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 Nio 0 5,661 1997/9 8 Bolivia, Colombia, Ecuador, Peru El Nio

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, Amrica 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 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 Philip Wijmans, LWF/ACT Mozambique, March 2000 70 5 4 60 3

50 2 40 1 0 30 Te m p e r a tu r e a n o m a lie s P e rc e n t o f m a la ria c a s e s in h o s p ita l Proportion of malaria cases and anomalies in maximum temperture: Kenya

-1 20 Jan 97May SepJan 98May SepJan 99May Sep -2 Time Malaria cases Dr. Githeko, 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] C h a n g e o f c o n s e c u t iv e m o n t h s 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 1 C increase in temperature Checkley et al. 2000 Number of Cholera cases in Uganda 1997-2002 50000 Number of cases 40000 El Nino stops El Nino starts

30000 20000 10000 0 1996 1997 1998 1999 2000 Time in years

Dr. Githeko, personal communication 2001 2002 2003 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 publications/cchhsummary/ 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 Health Data to Determine the Current Burden of ClimateSensitive Diseases 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? 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 se/en Ministry of Health

Disease surveillance/reporting branch Health Data Sources - Other UNICEF at CRED-EMDAT provides data on disasters 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 = 158/1000 72% of 1-year-olds immunized with 3 doses of DTP 5.8% of gross domestic product spent on health World Health Report 2005 WHO Region Afr-E (Countries with High Child & Very High Adult Mortality) Population 360,965,000 Total deaths

6,007,000 HIV/AIDS 1,616,000 Diarrheal diseases 356,000 Malaria 579,000 Protein-energy malnutrition

World Health Report 2004 54,000 Seychelles National Communication Methods and Tools for V&A Assessment in the Health Sector 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 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? Methods of Assessing Human Health Vulnerability and Public Health

Adaptation to Climate Change Kovats et al. 2003 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 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 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 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)?

Comparative Risk Assessment Greenhouse gas emissions scenarios Time 2020s Global climate modelling: 2050s 2080s Generates series of maps of predicted future climate Health impact model:

Estimates the change in relative risk of specific diseases 2020s Campbell-Lendrum et al. 2003 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) VBD 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 Source: UK Hadley Centre models Campbell-Lendrum et al. 2003 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 scenarios, s550 as function ofs750 GHG emissions 1.08

UE 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 non-climatic factors Campbell-Lendrum et al. 2003 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]

Climate change document will be published soon The website [] contains prevalence and population data, and regional and county-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 or can download components from website Proportion of Mosquitoes Surviving One Day Relationship Between Temperature and Daily Survivorship of Anopheles 1.00 0.90 0.80 0.70 0.60 0.50

0.40 0.30 0.20 0.10 0.00 Mean Temperature (C) Proportion Surviving Proportion of Vectors Surviving Time Required for Parasite Development 0.40 0.35 0.30 0.25 0.20

0.15 0.10 0.05 0.00 Mean Temperature (C) Relationship Between Temperature and Time Required for Parasite Development 120 100 Days 80 60 40 20

0 Mean Temperature (C ) 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. 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 Malaria in Zimbabwe Cases by Month Source: South African Malaria Research Programme Ebi et al. Climatic Change 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 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. Climatic Change 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

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 +0.6C Decrease in minimum temperatures 0.2 C Ebi et al. Climatic Change 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 Scenarios Climate sensitivity High = 4.5C Low = 1.4C 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 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 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 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 1

0.8 0.8 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 3.5 Fuzzy Value 1.2

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

Baseline Ebi et al. Climatic Change 2025 Ebi et al. Climatic Change 2050 Ebi et al. Climatic Change 2075 Ebi et al. Climatic Change 2100

Ebi et al. Climatic Change 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 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 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 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? 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 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 Policy Analysis of Flooding Adaptation Strategies, Policies and Measures in the UK Theoretical Range of

Choice Technically feasibility demonstrated? Economically feasible? Socially and Legally Acceptable? Effective to address health outcome?

Closed/Open (Practical Range of Choice) Land use planning to reduce risk exposure Yes at County and District levels only Yes Yes

Yes Open Engineering works to reduce risk exposure Yes Yes Yes Yes

Open Insurance Generally not available Emergency relief Yes Burton and Ebi, in preparation Closed Yes

Yes Yes Open Practical Range of Choice Size of Events/ Exposure Intensity Technically

viable? Economically possible (includes needed infrastructure available)? Institutional support and human capital available? Land use planning to reduce risk

exposure Yes Yes Over 400 local Variable planning authorities; little central coordination Variable No Engineering

works to reduce risk exposure Yes Grant aid to supplement local resources for flood defense is provided only for capital schemes Through Environment

Agency and County Councils Variable Variable No Emergency relief Yes Yes

County and District Councils; emergency services; local and regional health authorities Yes No No Burton and Ebi, in preparation

Compatible with current policies? Policy Transchange boundary needed? issue? Thank You

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